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添加fastlio和pointlio

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Robofish 2024-10-05 19:29:28 +08:00
parent 5b268043e2
commit 5d730aeaf3
117 changed files with 25932 additions and 0 deletions
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.catkin_workspace Normal file
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# This file currently only serves to mark the location of a catkin workspace for tool integration

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.gitignore vendored Normal file
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build/
devel/

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build.sh Normal file
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catkin_make

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src/CMakeLists.txt Symbolic link
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/opt/ros/noetic/share/catkin/cmake/toplevel.cmake

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src/FAST_LIO/.github/stale.yml vendored Normal file
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# Number of days of inactivity before an issue becomes stale
daysUntilStale: 21
# Number of days of inactivity before a stale issue is closed
daysUntilClose: 1
# Issues with these labels will never be considered stale
exemptLabels:
- pinned
- security
# Label to use when marking an issue as stale
staleLabel: stale
# Comment to post when marking an issue as stale. Set to `false` to disable
markComment: >
This issue has been automatically marked as stale because it has not had
recent activity. It will be closed if no further activity occurs. Thank you
for your contributions.
# Comment to post when closing a stale issue. Set to `false` to disable
closeComment: false

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src/FAST_LIO/.gitignore vendored Normal file
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build
Log/*.png
Log/*.txt
Log/*.csv
Log/*.pdf
.vscode/c_cpp_properties.json
.vscode/settings.json
PCD/*.pcd

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src/FAST_LIO/.gitmodules vendored Normal file
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[submodule "include/ikd-Tree"]
path = include/ikd-Tree
url = https://github.com/hku-mars/ikd-Tree.git
branch = fast_lio

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src/FAST_LIO/CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 2.8.3)
project(fast_lio)
SET(CMAKE_BUILD_TYPE "Debug")
ADD_COMPILE_OPTIONS(-std=c++14 )
ADD_COMPILE_OPTIONS(-std=c++14 )
set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )
add_definitions(-DROOT_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/\")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fexceptions" )
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -pthread -std=c++0x -std=c++14 -fexceptions")
message("Current CPU archtecture: ${CMAKE_SYSTEM_PROCESSOR}")
if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" )
include(ProcessorCount)
ProcessorCount(N)
message("Processer number: ${N}")
if(N GREATER 4)
add_definitions(-DMP_EN)
add_definitions(-DMP_PROC_NUM=3)
message("core for MP: 3")
elseif(N GREATER 3)
add_definitions(-DMP_EN)
add_definitions(-DMP_PROC_NUM=2)
message("core for MP: 2")
else()
add_definitions(-DMP_PROC_NUM=1)
endif()
else()
add_definitions(-DMP_PROC_NUM=1)
endif()
find_package(OpenMP QUIET)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
find_package(PythonLibs REQUIRED)
find_path(MATPLOTLIB_CPP_INCLUDE_DIRS "matplotlibcpp.h")
find_package(catkin REQUIRED COMPONENTS
geometry_msgs
nav_msgs
sensor_msgs
roscpp
rospy
std_msgs
pcl_ros
tf
livox_ros_driver2
message_generation
eigen_conversions
)
find_package(Eigen3 REQUIRED)
find_package(PCL 1.10 REQUIRED)
message(Eigen: ${EIGEN3_INCLUDE_DIR})
include_directories(
${catkin_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIR}
${PCL_INCLUDE_DIRS}
${PYTHON_INCLUDE_DIRS}
include)
add_message_files(
FILES
Pose6D.msg
)
generate_messages(
DEPENDENCIES
geometry_msgs
)
catkin_package(
CATKIN_DEPENDS geometry_msgs nav_msgs roscpp rospy std_msgs message_runtime
DEPENDS EIGEN3 PCL
INCLUDE_DIRS
)
add_executable(fastlio_mapping src/laserMapping.cpp include/ikd-Tree/ikd_Tree.cpp src/preprocess.cpp)
target_link_libraries(fastlio_mapping ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES})
target_include_directories(fastlio_mapping PRIVATE ${PYTHON_INCLUDE_DIRS})

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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The licenses for most software are designed to take away your
freedom to share and change it. By contrast, the GNU General Public
License is intended to guarantee your freedom to share and change free
software--to make sure the software is free for all its users. This
General Public License applies to most of the Free Software
Foundation's software and to any other program whose authors commit to
using it. (Some other Free Software Foundation software is covered by
the GNU Lesser General Public License instead.) You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
this service if you wish), that you receive source code or can get it
if you want it, that you can change the software or use pieces of it
in new free programs; and that you know you can do these things.
To protect your rights, we need to make restrictions that forbid
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These restrictions translate to certain responsibilities for you if you
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For example, if you distribute copies of such a program, whether
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you have. You must make sure that they, too, receive or can get the
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rights.
We protect your rights with two steps: (1) copyright the software, and
(2) offer you this license which gives you legal permission to copy,
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Also, for each author's protection and ours, we want to make certain
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Finally, any free program is threatened constantly by software
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program will individually obtain patent licenses, in effect making the
program proprietary. To prevent this, we have made it clear that any
patent must be licensed for everyone's free use or not licensed at all.
The precise terms and conditions for copying, distribution and
modification follow.
GNU GENERAL PUBLIC LICENSE
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
0. This License applies to any program or other work which contains
a notice placed by the copyright holder saying it may be distributed
under the terms of this General Public License. The "Program", below,
refers to any such program or work, and a "work based on the Program"
means either the Program or any derivative work under copyright law:
that is to say, a work containing the Program or a portion of it,
either verbatim or with modifications and/or translated into another
language. (Hereinafter, translation is included without limitation in
the term "modification".) Each licensee is addressed as "you".
Activities other than copying, distribution and modification are not
covered by this License; they are outside its scope. The act of
running the Program is not restricted, and the output from the Program
is covered only if its contents constitute a work based on the
Program (independent of having been made by running the Program).
Whether that is true depends on what the Program does.
1. You may copy and distribute verbatim copies of the Program's
source code as you receive it, in any medium, provided that you
conspicuously and appropriately publish on each copy an appropriate
copyright notice and disclaimer of warranty; keep intact all the
notices that refer to this License and to the absence of any warranty;
and give any other recipients of the Program a copy of this License
along with the Program.
You may charge a fee for the physical act of transferring a copy, and
you may at your option offer warranty protection in exchange for a fee.
2. You may modify your copy or copies of the Program or any portion
of it, thus forming a work based on the Program, and copy and
distribute such modifications or work under the terms of Section 1
above, provided that you also meet all of these conditions:
a) You must cause the modified files to carry prominent notices
stating that you changed the files and the date of any change.
b) You must cause any work that you distribute or publish, that in
whole or in part contains or is derived from the Program or any
part thereof, to be licensed as a whole at no charge to all third
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c) If the modified program normally reads commands interactively
when run, you must cause it, when started running for such
interactive use in the most ordinary way, to print or display an
announcement including an appropriate copyright notice and a
notice that there is no warranty (or else, saying that you provide
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these conditions, and telling the user how to view a copy of this
License. (Exception: if the Program itself is interactive but
does not normally print such an announcement, your work based on
the Program is not required to print an announcement.)
These requirements apply to the modified work as a whole. If
identifiable sections of that work are not derived from the Program,
and can be reasonably considered independent and separate works in
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Thus, it is not the intent of this section to claim rights or contest
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exercise the right to control the distribution of derivative or
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In addition, mere aggregation of another work not based on the Program
with the Program (or with a work based on the Program) on a volume of
a storage or distribution medium does not bring the other work under
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3. You may copy and distribute the Program (or a work based on it,
under Section 2) in object code or executable form under the terms of
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a) Accompany it with the complete corresponding machine-readable
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customarily used for software interchange; or,
c) Accompany it with the information you received as to the offer
to distribute corresponding source code. (This alternative is
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distribution of the source code, even though third parties are not
compelled to copy the source along with the object code.
4. You may not copy, modify, sublicense, or distribute the Program
except as expressly provided under this License. Any attempt
otherwise to copy, modify, sublicense or distribute the Program is
void, and will automatically terminate your rights under this License.
However, parties who have received copies, or rights, from you under
this License will not have their licenses terminated so long as such
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5. You are not required to accept this License, since you have not
signed it. However, nothing else grants you permission to modify or
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prohibited by law if you do not accept this License. Therefore, by
modifying or distributing the Program (or any work based on the
Program), you indicate your acceptance of this License to do so, and
all its terms and conditions for copying, distributing or modifying
the Program or works based on it.
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Program), the recipient automatically receives a license from the
original licensor to copy, distribute or modify the Program subject to
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infringement or for any other reason (not limited to patent issues),
conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
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License and any other pertinent obligations, then as a consequence you
may not distribute the Program at all. For example, if a patent
license would not permit royalty-free redistribution of the Program by
all those who receive copies directly or indirectly through you, then
the only way you could satisfy both it and this License would be to
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any particular circumstance, the balance of the section is intended to
apply and the section as a whole is intended to apply in other
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It is not the purpose of this section to induce you to infringe any
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such claims; this section has the sole purpose of protecting the
integrity of the free software distribution system, which is
implemented by public license practices. Many people have made
generous contributions to the wide range of software distributed
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system; it is up to the author/donor to decide if he or she is willing
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This section is intended to make thoroughly clear what is believed to
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certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
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NO WARRANTY
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FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
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OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.

135
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clear
close all
Color_red = [0.6350 0.0780 0.1840];
Color_blue = [0 0.4470 0.7410];
Color_orange = [0.8500 0.3250 0.0980];
Color_green = [0.4660 0.6740 0.1880];
Color_lightblue = [0.3010 0.7450 0.9330];
Color_purple = [0.4940 0.1840 0.5560];
Color_yellow = [0.9290 0.6940 0.1250];
fast_lio_ikdtree = csvread("./fast_lio_time_log.csv",1,0);
timestamp_ikd = fast_lio_ikdtree(:,1);
timestamp_ikd = timestamp_ikd - min(timestamp_ikd);
total_time_ikd = fast_lio_ikdtree(:,2)*1e3;
scan_num = fast_lio_ikdtree(:,3);
incremental_time_ikd = fast_lio_ikdtree(:,4)*1e3;
search_time_ikd = fast_lio_ikdtree(:,5)*1e3;
delete_size_ikd = fast_lio_ikdtree(:,6);
delete_time_ikd = fast_lio_ikdtree(:,7) * 1e3;
tree_size_ikd_st = fast_lio_ikdtree(:,8);
tree_size_ikd = fast_lio_ikdtree(:,9);
add_points = fast_lio_ikdtree(:,10);
fast_lio_forest = csvread("fast_lio_time_log.csv",1,0);
fov_check_time_forest = fast_lio_forest(:,5)*1e3;
average_time_forest = fast_lio_forest(:,2)*1e3;
total_time_forest = fast_lio_forest(:,6)*1e3;
incremental_time_forest = fast_lio_forest(:,3)*1e3;
search_time_forest = fast_lio_forest(:,4)*1e3;
timestamp_forest = fast_lio_forest(:,1);
% Use slide window to calculate average
L = 1; % Length of slide window
for i = 1:length(timestamp_ikd)
if (i<L)
average_time_ikd(i) = mean(total_time_ikd(1:i));
else
average_time_ikd(i) = mean(total_time_ikd(i-L+1:i));
end
end
for i = 1:length(timestamp_forest)
if (i<L)
average_time_forest(i) = mean(total_time_forest(1:i));
else
average_time_forest(i) = mean(total_time_forest(i-L+1:i));
end
end
f = figure;
set(gcf,'Position',[80 433 600 640])
tiled_handler = tiledlayout(3,1);
tiled_handler.TileSpacing = 'compact';
tiled_handler.Padding = 'compact';
nexttile;
hold on;
set(gca,'FontSize',12,'FontName','Times New Roman')
plot(timestamp_ikd, average_time_ikd,'-','Color',Color_blue,'LineWidth',1.2);
plot(timestamp_forest, average_time_forest,'--','Color',Color_orange,'LineWidth',1.2);
lg = legend("ikd-Tree", "ikd-Forest",'location',[0.1314 0.8559 0.2650 0.0789],'fontsize',14,'fontname','Times New Roman')
title("Time Performance on FAST-LIO",'FontSize',16,'FontName','Times New Roman')
xlabel("time/s",'FontSize',16,'FontName','Times New Roman')
yl = ylabel("Run Time/ms",'FontSize',15,'Position',[285.7 5.5000 -1]);
xlim([32,390]);
ylim([0,23]);
ax1 = gca;
ax1.YAxis.FontSize = 12;
ax1.XAxis.FontSize = 12;
grid on
box on
% print('./Figures/fastlio_exp_average','-depsc','-r600')
index_ikd = find(search_time_ikd > 0);
search_time_ikd = search_time_ikd(index_ikd);
index_forest = find(search_time_forest > 0);
search_time_forest = search_time_forest(index_forest);
t = nexttile;
hold on;
boxplot_data_ikd = [incremental_time_ikd,total_time_ikd];
boxplot_data_forest = [incremental_time_forest,total_time_forest];
Colors_ikd = [Color_blue;Color_blue;Color_blue];
Colors_forest = [Color_orange;Color_orange;Color_orange];
% xticks([3,8,13])
h_search_ikd = boxplot(search_time_ikd,'Whisker',50,'Positions',1,'Colors',Color_blue,'Widths',0.3);
h_search_forest = boxplot(search_time_forest,'Whisker',50,'Positions',1.5,'Colors',Color_orange,'Widths',0.3);
h_ikd = boxplot(boxplot_data_ikd,'Whisker',50,'Positions',[3,5],'Colors',Color_blue,'Widths',0.3);
h_forest = boxplot(boxplot_data_forest,'Whisker',50,'Positions',[3.5,5.5],'Colors',Color_orange,'Widths',0.3);
ax2 = gca;
ax2.YAxis.Scale = 'log';
xlim([0.5,6.0])
ylim([0.0008,100])
xticks([1.25 3.25 5.25])
xticklabels({'Nearest Search',' Incremental Updates','Total Time'});
yticks([1e-3,1e-2,1e-1,1e0,1e1,1e2])
ax2.YAxis.FontSize = 12;
ax2.XAxis.FontSize = 14.5;
% ax.XAxis.FontWeight = 'bold';
ylabel('Run Time/ms','FontSize',14,'FontName','Times New Roman')
box_vars = [findall(h_search_ikd,'Tag','Box');findall(h_ikd,'Tag','Box');findall(h_search_forest,'Tag','Box');findall(h_forest,'Tag','Box')];
for j=1:length(box_vars)
if (j<=3)
Color = Color_blue;
else
Color = Color_orange;
end
patch(get(box_vars(j),'XData'),get(box_vars(j),'YData'),Color,'FaceAlpha',0.25,'EdgeColor',Color);
end
Lg = legend(box_vars([1,4]), {'ikd-Tree','ikd-Forest'},'Location',[0.6707 0.4305 0.265 0.07891],'fontsize',14,'fontname','Times New Roman');
grid on
set(gca,'YMinorGrid','off')
nexttile;
hold on;
grid on;
box on;
set(gca,'FontSize',12,'FontName','Times New Roman')
plot(timestamp_ikd, alpha_bal_ikd,'-','Color',Color_blue,'LineWidth',1.2);
plot(timestamp_ikd, alpha_del_ikd,'--','Color',Color_orange, 'LineWidth', 1.2);
plot(timestamp_ikd, 0.6*ones(size(alpha_bal_ikd)), ':','Color','black','LineWidth',1.2);
lg = legend("\alpha_{bal}", "\alpha_{del}",'location',[0.7871 0.1131 0.1433 0.069],'fontsize',14,'fontname','Times New Roman')
title("Re-balancing Criterion",'FontSize',16,'FontName','Times New Roman')
xlabel("time/s",'FontSize',16,'FontName','Times New Roman')
yl = ylabel("\alpha",'FontSize',15, 'Position',[285.7 0.4250 -1])
xlim([32,390]);
ylim([0,0.85]);
ax3 = gca;
ax3.YAxis.FontSize = 12;
ax3.XAxis.FontSize = 12;
% print('./Figures/fastlio_exp_combine','-depsc','-r1200')
% exportgraphics(f,'./Figures/fastlio_exp_combine_1.pdf','ContentType','vector')

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Here saved the debug records which can be drew by the ../Log/plot.py. The record function can be found frm the MACRO: DEBUG_FILE_DIR(name) in common_lib.h.

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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
#######for ikfom
fig, axs = plt.subplots(4,2)
lab_pre = ['', 'pre-x', 'pre-y', 'pre-z']
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
a_pre=np.loadtxt('mat_pre.txt')
a_out=np.loadtxt('mat_out.txt')
time=a_pre[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Extrins-R')
axs[3,0].set_title('Extrins-T')
axs[0,1].set_title('Velocity')
axs[1,1].set_title('bg')
axs[2,1].set_title('ba')
axs[3,1].set_title('Gravity')
for i in range(1,4):
for j in range(8):
axs[j%4, j/4].plot(time, a_pre[:,i+j*3],'.-', label=lab_pre[i])
axs[j%4, j/4].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(8):
# axs[j].set_xlim(386,389)
axs[j%4, j/4].grid()
axs[j%4, j/4].legend()
plt.grid()
#######for ikfom#######
#### Draw IMU data
# fig, axs = plt.subplots(2)
# imu=np.loadtxt('imu.txt')
# time=imu[:,0]
# axs[0].set_title('Gyroscope')
# axs[1].set_title('Accelerameter')
# lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
# lab_2 = ['acc-x', 'acc-y', 'acc-z']
# for i in range(3):
# # if i==1:
# axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
# axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
# for i in range(2):
# # axs[i].set_xlim(386,389)
# axs[i].grid()
# axs[i].legend()
# plt.grid()
# #### Draw time calculation
# plt.figure(3)
# fig = plt.figure()
# font1 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 12,
# }
# c="red"
# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt')
# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt')
# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt')
# # n = a_out[:,1].size
# # time_mean = a_out[:,1].mean()
# # time_se = a_out[:,1].std() / np.sqrt(n)
# # time_err = a_out[:,1] - time_mean
# # feat_mean = a_out[:,2].mean()
# # feat_err = a_out[:,2] - feat_mean
# # feat_se = a_out[:,2].std() / np.sqrt(n)
# ax1 = fig.add_subplot(111)
# ax1.set_ylabel('Effective Feature Numbers',font1)
# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9])
# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9])
# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9])
# ax1.set_ylim([0, 3000])
# ax2 = ax1.twinx()
# ax2.spines['right'].set_color('red')
# ax2.set_ylabel('Compute Time (ms)',font1)
# ax2.yaxis.label.set_color('red')
# ax2.tick_params(axis='y', colors='red')
# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.set_xlim([0.5, 3.5])
# ax2.set_ylim([0, 100])
# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2'))
# # # print(time_se)
# # # print(a_out3[:,2])
# plt.grid()
# plt.savefig("time.pdf", dpi=1200)
plt.show()

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## Related Works and Extended Application
**SLAM:**
1. [ikd-Tree](https://github.com/hku-mars/ikd-Tree): A state-of-art dynamic KD-Tree for 3D kNN search.
2. [R2LIVE](https://github.com/hku-mars/r2live): A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
3. [LI_Init](https://github.com/hku-mars/LiDAR_IMU_Init): A robust, real-time LiDAR-IMU extrinsic initialization and synchronization package..
4. [FAST-LIO-LOCALIZATION](https://github.com/HViktorTsoi/FAST_LIO_LOCALIZATION): The integration of FAST-LIO with **Re-localization** function module.
**Control and Plan:**
1. [IKFOM](https://github.com/hku-mars/IKFoM): A Toolbox for fast and high-precision on-manifold Kalman filter.
2. [UAV Avoiding Dynamic Obstacles](https://github.com/hku-mars/dyn_small_obs_avoidance): One of the implementation of FAST-LIO in robot's planning.
3. [UGV Demo](https://www.youtube.com/watch?v=wikgrQbE6Cs): Model Predictive Control for Trajectory Tracking on Differentiable Manifolds.
4. [Bubble Planner](https://arxiv.org/abs/2202.12177): Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors.
<!-- 10. [**FAST-LIVO**](https://github.com/hku-mars/FAST-LIVO): Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry. -->
## FAST-LIO
**FAST-LIO** (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. Our package address many key issues:
1. Fast iterated Kalman filter for odometry optimization;
2. Automaticaly initialized at most steady environments;
3. Parallel KD-Tree Search to decrease the computation;
## FAST-LIO 2.0 (2021-07-05 Update)
<!-- ![image](doc/real_experiment2.gif) -->
<!-- [![Watch the video](doc/real_exp_2.png)](https://youtu.be/2OvjGnxszf8) -->
<div align="left">
<img src="doc/real_experiment2.gif" width=49.6% />
<img src="doc/ulhkwh_fastlio.gif" width = 49.6% >
</div>
**Related video:** [FAST-LIO2](https://youtu.be/2OvjGnxszf8), [FAST-LIO1](https://youtu.be/iYCY6T79oNU)
**Pipeline:**
<div align="center">
<img src="doc/overview_fastlio2.svg" width=99% />
</div>
**New Features:**
1. Incremental mapping using [ikd-Tree](https://github.com/hku-mars/ikd-Tree), achieve faster speed and over 100Hz LiDAR rate.
2. Direct odometry (scan to map) on Raw LiDAR points (feature extraction can be disabled), achieving better accuracy.
3. Since no requirements for feature extraction, FAST-LIO2 can support many types of LiDAR including spinning (Velodyne, Ouster) and solid-state (Livox Avia, Horizon, MID-70) LiDARs, and can be easily extended to support more LiDARs.
4. Support external IMU.
5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry Pi 4B(8G RAM).
**Related papers**:
[FAST-LIO2: Fast Direct LiDAR-inertial Odometry](doc/Fast_LIO_2.pdf)
[FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://arxiv.org/abs/2010.08196)
**Contributors**
[Wei Xu 徐威](https://github.com/XW-HKU)，[Yixi Cai 蔡逸熙](https://github.com/Ecstasy-EC)，[Dongjiao He 贺东娇](https://github.com/Joanna-HE)，[Fangcheng Zhu 朱方程](https://github.com/zfc-zfc)，[Jiarong Lin 林家荣](https://github.com/ziv-lin)，[Zheng Liu 刘政](https://github.com/Zale-Liu), [Borong Yuan](https://github.com/borongyuan)
<!-- <div align="center">
<img src="doc/results/HKU_HW.png" width = 49% >
<img src="doc/results/HKU_MB_001.png" width = 49% >
</div> -->
## 1. Prerequisites
### 1.1 **Ubuntu** and **ROS**
**Ubuntu >= 16.04**
For **Ubuntu 18.04 or higher**, the **default** PCL and Eigen is enough for FAST-LIO to work normally.
ROS >= Melodic. [ROS Installation](http://wiki.ros.org/ROS/Installation)
### 1.2. **PCL && Eigen**
PCL >= 1.8, Follow [PCL Installation](http://www.pointclouds.org/downloads/linux.html).
Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
### 1.3. **livox_ros_driver**
Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
*Remarks:*
- Since the FAST-LIO must support Livox serials LiDAR firstly, so the **livox_ros_driver** must be installed and **sourced** before run any FAST-LIO luanch file.
- How to source? The easiest way is add the line ``` source $Livox_ros_driver_dir$/devel/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Livox_ros_driver_dir$ ``` is the directory of the livox ros driver workspace (should be the ``` ws_livox ``` directory if you completely followed the livox official document).
## 2. Build
If you want to use docker conatiner to run fastlio2, please install the docker on you machine.
Follow [Docker Installation](https://docs.docker.com/engine/install/ubuntu/).
### 2.1 Docker Container
User can create a new script with anyname by the following command in linux:
```
touch <your_custom_name>.sh
```
Place the following code inside the ``` <your_custom_name>.sh ``` script.
```
#!/bin/bash
mkdir docker_ws
# Script to run ROS Kinetic with GUI support in Docker
# Allow X server to be accessed from the local machine
xhost +local:
# Container name
CONTAINER_NAME="fastlio2"
# Run the Docker container
docker run -itd \
--name=$CONTAINER_NAME \
--user mars_ugv \
--network host \
--ipc=host \
-v /home/$USER/docker_ws:/home/mars_ugv/docker_ws \
--privileged \
--env="QT_X11_NO_MITSHM=1" \
--volume="/etc/localtime:/etc/localtime:ro" \
-v /dev/bus/usb:/dev/bus/usb \
--device=/dev/dri \
--group-add video \
-v /tmp/.X11-unix:/tmp/.X11-unix \
--env="DISPLAY=$DISPLAY" \
kenny0407/marslab_fastlio2:latest \
/bin/bash
```
execute the following command to grant execute permissions to the script, making it runnable:
```
sudo chmod +x <your_custom_name>.sh
```
execute the following command to download the image and create the container.
```
./<your_custom_name>.sh
```
*Script explanation:*
- The docker run command provided below creates a container with a tag, using an image from Docker Hub. The download duration for this image can differ depending on the user's network speed.
- This command also establishes a new workspace called ``` docker_ws ```, which serves as a shared folder between the Docker container and the host machine. This means that if users wish to run the rosbag example, they need to download the rosbag file and place it in the ``` docker_ws ``` directory on their host machine.
- Subsequently, a folder with the same name inside the Docker container will receive this file. Users can then easily play the file within Docker.
- In this example, we've shared the network of the host machine with the Docker container. Consequently, if users execute the ``` rostopic list ``` command, they will observe identical output whether they run it on the host machine or inside the Docker container."
### 2.2 Build from source
Clone the repository and catkin_make:
```
cd ~/$A_ROS_DIR$/src
git clone https://github.com/hku-mars/FAST_LIO.git
cd FAST_LIO
git submodule update --init
cd ../..
catkin_make
source devel/setup.bash
```
- Remember to source the livox_ros_driver before build (follow 1.3 **livox_ros_driver**)
- If you want to use a custom build of PCL, add the following line to ~/.bashrc
```export PCL_ROOT={CUSTOM_PCL_PATH}```
## 3. Directly run
Noted:
A. Please make sure the IMU and LiDAR are **Synchronized**, that's important.
B. The warning message "Failed to find match for field 'time'." means the timestamps of each LiDAR points are missed in the rosbag file. That is important for the forward propagation and backwark propagation.
C. We recommend to set the **extrinsic_est_en** to false if the extrinsic is give. As for the extrinsic initiallization, please refer to our recent work: [**Robust Real-time LiDAR-inertial Initialization**](https://github.com/hku-mars/LiDAR_IMU_Init).
### 3.1 For Avia
Connect to your PC to Livox Avia LiDAR by following [Livox-ros-driver installation](https://github.com/Livox-SDK/livox_ros_driver), then
```
cd ~/$FAST_LIO_ROS_DIR$
source devel/setup.bash
roslaunch fast_lio mapping_avia.launch
roslaunch livox_ros_driver livox_lidar_msg.launch
```
- For livox serials, FAST-LIO only support the data collected by the ``` livox_lidar_msg.launch ``` since only its ``` livox_ros_driver/CustomMsg ``` data structure produces the timestamp of each LiDAR point which is very important for the motion undistortion. ``` livox_lidar.launch ``` can not produce it right now.
- If you want to change the frame rate, please modify the **publish_freq** parameter in the [livox_lidar_msg.launch](https://github.com/Livox-SDK/livox_ros_driver/blob/master/livox_ros_driver/launch/livox_lidar_msg.launch) of [Livox-ros-driver](https://github.com/Livox-SDK/livox_ros_driver) before make the livox_ros_driver pakage.
### 3.2 For Livox serials with external IMU
mapping_avia.launch theratically supports mid-70, mid-40 or other livox serial LiDAR, but need to setup some parameters befor run:
Edit ``` config/avia.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ```
3. Translational extrinsic: ``` extrinsic_T ```
4. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame). They can be found in the official manual.
- FAST-LIO produces a very simple software time sync for livox LiDAR, set parameter ```time_sync_en``` to ture to turn on. But turn on **ONLY IF external time synchronization is really not possible**, since the software time sync cannot make sure accuracy.
### 3.3 For Velodyne or Ouster (Velodyne as an example)
Step A: Setup before run
Edit ``` config/velodyne.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ``` (both internal and external, 6-aixes or 9-axies are fine)
3. Set the parameter ```timestamp_unit``` based on the unit of **time** (Velodyne) or **t** (Ouster) field in PoindCloud2 rostopic
4. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): ``` scan_line ```
5. Translational extrinsic: ``` extrinsic_T ```
6. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).
Step B: Run below
```
cd ~/$FAST_LIO_ROS_DIR$
source devel/setup.bash
roslaunch fast_lio mapping_velodyne.launch
```
Step C: Run LiDAR's ros driver or play rosbag.
### 3.4 PCD file save
Set ``` pcd_save_enable ``` in launchfile to ``` 1 ```. All the scans (in global frame) will be accumulated and saved to the file ``` FAST_LIO/PCD/scans.pcd ``` after the FAST-LIO is terminated. ```pcl_viewer scans.pcd``` can visualize the point clouds.
*Tips for pcl_viewer:*
- change what to visualize/color by pressing keyboard 1,2,3,4,5 when pcl_viewer is running.
```
1 is all random
2 is X values
3 is Y values
4 is Z values
5 is intensity
```
## 4. Rosbag Example
### 4.1 Livox Avia Rosbag
<div align="left">
<img src="doc/results/HKU_LG_Indoor.png" width=47% />
<img src="doc/results/HKU_MB_002.png" width = 51% >
Files: Can be downloaded from [google drive](https://drive.google.com/drive/folders/1CGYEJ9-wWjr8INyan6q1BZz_5VtGB-fP?usp=sharing)
Run:
```
roslaunch fast_lio mapping_avia.launch
rosbag play YOUR_DOWNLOADED.bag
```
### 4.2 Velodyne HDL-32E Rosbag
**NCLT Dataset**: Original bin file can be found [here](http://robots.engin.umich.edu/nclt/).
We produce [Rosbag Files](https://drive.google.com/drive/folders/1blQJuAB4S80NwZmpM6oALyHWvBljPSOE?usp=sharing) and [a python script](https://drive.google.com/file/d/1QC9IRBv2_-cgo_AEvL62E1ml1IL9ht6J/view?usp=sharing) to generate Rosbag files: ```python3 sensordata_to_rosbag_fastlio.py bin_file_dir bag_name.bag```
Run:
```
roslaunch fast_lio mapping_velodyne.launch
rosbag play YOUR_DOWNLOADED.bag
```
## 5.Implementation on UAV
In order to validate the robustness and computational efficiency of FAST-LIO in actual mobile robots, we build a small-scale quadrotor which can carry a Livox Avia LiDAR with 70 degree FoV and a DJI Manifold 2-C onboard computer with a 1.8 GHz Intel i7-8550U CPU and 8 G RAM, as shown in below.
The main structure of this UAV is 3d printed (Aluminum or PLA), the .stl file will be open-sourced in the future.
<div align="center">
<img src="doc/uav01.jpg" width=40.5% >
<img src="doc/uav_system.png" width=57% >
</div>
## 6.Acknowledgments
Thanks for LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), [Livox_Mapping](https://github.com/Livox-SDK/livox_mapping), [LINS](https://github.com/ChaoqinRobotics/LINS---LiDAR-inertial-SLAM) and [Loam_Livox](https://github.com/hku-mars/loam_livox).

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
time_sync_en: false # ONLY turn on when external time synchronization is really not possible
time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
# This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0
preprocess:
lidar_type: 1 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
scan_line: 6
blind: 4
mapping:
acc_cov: 0.1
gyr_cov: 0.1
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
fov_degree: 90
det_range: 450.0
extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic
extrinsic_T: [ 0.04165, 0.02326, -0.0284 ]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1]
publish:
path_en: false
scan_publish_en: true # false: close all the point cloud output
dense_publish_en: true # false: low down the points number in a global-frame point clouds scan.
scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
time_sync_en: false # ONLY turn on when external time synchronization is really not possible
time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
# This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0
preprocess:
lidar_type: 1 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
scan_line: 6
blind: 4
mapping:
acc_cov: 0.1
gyr_cov: 0.1
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
fov_degree: 100
det_range: 260.0
extrinsic_est_en: true # true: enable the online estimation of IMU-LiDAR extrinsic
extrinsic_T: [ 0.05512, 0.02226, -0.0297 ]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1]
publish:
path_en: false
scan_publish_en: true # false: close all the point cloud output
dense_publish_en: true # false: low down the points number in a global-frame point clouds scan.
scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
time_sync_en: false # ONLY turn on when external time synchronization is really not possible
time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
# This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0
preprocess:
lidar_type: 1 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
scan_line: 4
blind: 0.3
mapping:
acc_cov: 0.1
gyr_cov: 0.1
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
fov_degree: 360
det_range: 100.0
extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic
extrinsic_T: [ -0.011, -0.02329, 0.04412 ]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1]
publish:
path_en: false
scan_publish_en: true # false: close all the point cloud output
dense_publish_en: true # false: low down the points number in a global-frame point clouds scan.
scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
time_sync_en: false # ONLY turn on when external time synchronization is really not possible
time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
# This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0
preprocess:
lidar_type: 1 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
scan_line: 4
blind: 0.1
mapping:
acc_cov: 0.1
gyr_cov: 0.1
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
fov_degree: 360
det_range: 100.0
extrinsic_est_en: true # true: enable the online estimation of IMU-LiDAR extrinsic
extrinsic_T: [ -0.011, -0.02329, 0.04412 ]
extrinsic_R: [ 1, 0, 0,
0, -1, 0,
0, 0, -1]
publish:
path_en: false
scan_publish_en: true # false: close all the point cloud output
dense_publish_en: true # false: low down the points number in a global-frame point clouds scan.
scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/os_cloud_node/points"
imu_topic: "/os_cloud_node/imu"
time_sync_en: false # ONLY turn on when external time synchronization is really not possible
time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
# This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0
preprocess:
lidar_type: 3 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
scan_line: 64
timestamp_unit: 3 # 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 4
mapping:
acc_cov: 0.1
gyr_cov: 0.1
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
fov_degree: 180
det_range: 150.0
extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic
extrinsic_T: [ 0.0, 0.0, 0.0 ]
extrinsic_R: [1, 0, 0,
0, 1, 0,
0, 0, 1]
publish:
path_en: false
scan_publish_en: true # false: close all the point cloud output
dense_publish_en: true # false: low down the points number in a global-frame point clouds scan.
scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/velodyne_points"
imu_topic: "/imu/data"
time_sync_en: false # ONLY turn on when external time synchronization is really not possible
time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
# This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0
preprocess:
lidar_type: 2 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
scan_line: 32
scan_rate: 10 # only need to be set for velodyne, unit: Hz,
timestamp_unit: 2 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 2
mapping:
acc_cov: 0.1
gyr_cov: 0.1
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
fov_degree: 180
det_range: 100.0
extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic,
extrinsic_T: [ 0, 0, 0.28]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1]
publish:
path_en: false
scan_publish_en: true # false: close all the point cloud output
dense_publish_en: true # false: low down the points number in a global-frame point clouds scan.
scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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#ifndef EXP_MAT_H
#define EXP_MAT_H
#include <math.h>
#include <Eigen/Core>
#include <opencv2/core.hpp>
// #include <common_lib.h>
#define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &&ang)
{
T ang_norm = ang.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang / ang_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
/// Roderigous Tranformation
return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T, typename Ts>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &ang_vel, const Ts &dt)
{
T ang_vel_norm = ang_vel.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_vel_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang_vel / ang_vel_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
T r_ang = ang_vel_norm * dt;
/// Roderigous Tranformation
return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const T &v1, const T &v2, const T &v3)
{
T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3);
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (norm > 0.00001)
{
T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm};
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_ang);
/// Roderigous Tranformation
return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K;
}
else
{
return Eye3;
}
}
/* Logrithm of a Rotation Matrix */
template<typename T>
Eigen::Matrix<T,3,1> Log(const Eigen::Matrix<T, 3, 3> &R)
{
T &&theta = std::acos(0.5 * (R.trace() - 1));
Eigen::Matrix<T,3,1> K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1));
return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K);
}
// template<typename T>
// cv::Mat Exp(const T &v1, const T &v2, const T &v3)
// {
// T norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3);
// cv::Mat Eye3 = cv::Mat::eye(3, 3, CV_32F);
// if (norm > 0.0000001)
// {
// T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm};
// cv::Mat K = (cv::Mat_<T>(3,3) << SKEW_SYM_MATRX(r_ang));
// /// Roderigous Tranformation
// return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K;
// }
// else
// {
// return Eye3;
// }
// }
#endif

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/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Author: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef __MEKFOM_UTIL_HPP__
#define __MEKFOM_UTIL_HPP__
#include <Eigen/Core>
#include "../mtk/src/mtkmath.hpp"
namespace esekfom {
template <typename T1, typename T2>
class is_same {
public:
operator bool() {
return false;
}
};
template<typename T1>
class is_same<T1, T1> {
public:
operator bool() {
return true;
}
};
template <typename T>
class is_double {
public:
operator bool() {
return false;
}
};
template<>
class is_double<double> {
public:
operator bool() {
return true;
}
};
template<typename T>
static T
id(const T &x)
{
return x;
}
} // namespace esekfom
#endif // __MEKFOM_UTIL_HPP__

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/build_manifold.hpp
* @brief Macro to automatically construct compound manifolds.
*
*/
#ifndef MTK_AUTOCONSTRUCT_HPP_
#define MTK_AUTOCONSTRUCT_HPP_
#include <vector>
#include <boost/preprocessor/seq.hpp>
#include <boost/preprocessor/cat.hpp>
#include <Eigen/Core>
#include "src/SubManifold.hpp"
#include "startIdx.hpp"
#ifndef PARSED_BY_DOXYGEN
//////// internals //////
#define MTK_APPLY_MACRO_ON_TUPLE(r, macro, tuple) macro tuple
#define MTK_TRANSFORM_COMMA(macro, entries) BOOST_PP_SEQ_ENUM(BOOST_PP_SEQ_TRANSFORM_S(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries))
#define MTK_TRANSFORM(macro, entries) BOOST_PP_SEQ_FOR_EACH_R(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries)
#define MTK_CONSTRUCTOR_ARG( type, id) const type& id = type()
#define MTK_CONSTRUCTOR_COPY( type, id) id(id)
#define MTK_BOXPLUS( type, id) id.boxplus(MTK::subvector(__vec, &self::id), __scale);
#define MTK_OPLUS( type, id) id.oplus(MTK::subvector_(__vec, &self::id), __scale);
#define MTK_BOXMINUS( type, id) id.boxminus(MTK::subvector(__res, &self::id), __oth.id);
#define MTK_S2_hat( type, id) if(id.IDX == idx){id.S2_hat(res);}
#define MTK_S2_Nx_yy( type, id) if(id.IDX == idx){id.S2_Nx_yy(res);}
#define MTK_S2_Mx( type, id) if(id.IDX == idx){id.S2_Mx(res, dx);}
#define MTK_OSTREAM( type, id) << __var.id << " "
#define MTK_ISTREAM( type, id) >> __var.id
#define MTK_S2_state( type, id) if(id.TYP == 1){S2_state.push_back(std::make_pair(id.IDX, id.DIM));}
#define MTK_SO3_state( type, id) if(id.TYP == 2){(SO3_state).push_back(std::make_pair(id.IDX, id.DIM));}
#define MTK_vect_state( type, id) if(id.TYP == 0){(vect_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));}
#define MTK_SUBVARLIST(seq, S2state, SO3state) \
BOOST_PP_FOR_1( \
( \
BOOST_PP_SEQ_SIZE(seq), \
BOOST_PP_SEQ_HEAD(seq), \
BOOST_PP_SEQ_TAIL(seq) (~), \
0,\
0,\
S2state,\
SO3state ),\
MTK_ENTRIES_TEST, MTK_ENTRIES_NEXT, MTK_ENTRIES_OUTPUT)
#define MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state) \
MTK::SubManifold<type, dof, dim> id;
#define MTK_PUT_TYPE_AND_ENUM(type, id, dof, dim, S2state, SO3state) \
MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state) \
enum {DOF = type::DOF + dof}; \
enum {DIM = type::DIM+dim}; \
typedef type::scalar scalar;
#define MTK_ENTRIES_OUTPUT(r, state) MTK_ENTRIES_OUTPUT_I state
#define MTK_ENTRIES_OUTPUT_I(s, head, seq, dof, dim, S2state, SO3state) \
MTK_APPLY_MACRO_ON_TUPLE(~, \
BOOST_PP_IF(BOOST_PP_DEC(s), MTK_PUT_TYPE, MTK_PUT_TYPE_AND_ENUM), \
( BOOST_PP_TUPLE_REM_2 head, dof, dim, S2state, SO3state))
#define MTK_ENTRIES_TEST(r, state) MTK_TUPLE_ELEM_4_0 state
//! this used to be BOOST_PP_TUPLE_ELEM_4_0:
#define MTK_TUPLE_ELEM_4_0(a,b,c,d,e,f, g) a
#define MTK_ENTRIES_NEXT(r, state) MTK_ENTRIES_NEXT_I state
#define MTK_ENTRIES_NEXT_I(len, head, seq, dof, dim, S2state, SO3state) ( \
BOOST_PP_DEC(len), \
BOOST_PP_SEQ_HEAD(seq), \
BOOST_PP_SEQ_TAIL(seq), \
dof + BOOST_PP_TUPLE_ELEM_2_0 head::DOF,\
dim + BOOST_PP_TUPLE_ELEM_2_0 head::DIM,\
S2state,\
SO3state)
#endif /* not PARSED_BY_DOXYGEN */
/**
* Construct a manifold.
* @param name is the class-name of the manifold,
* @param entries is the list of sub manifolds
*
* Entries must be given in a list like this:
* @code
* typedef MTK::trafo<MTK::SO3<double> > Pose;
* typedef MTK::vect<double, 3> Vec3;
* MTK_BUILD_MANIFOLD(imu_state,
* ((Pose, pose))
* ((Vec3, vel))
* ((Vec3, acc_bias))
* )
* @endcode
* Whitespace is optional, but the double parentheses are necessary.
* Construction is done entirely in preprocessor.
* After construction @a name is also a manifold. Its members can be
* accessed by names given in @a entries.
*
* @note Variable types are not allowed to have commas, thus types like
* @c vect<double, 3> need to be typedef'ed ahead.
*/
#define MTK_BUILD_MANIFOLD(name, entries) \
struct name { \
typedef name self; \
std::vector<std::pair<int, int> > S2_state;\
std::vector<std::pair<int, int> > SO3_state;\
std::vector<std::pair<std::pair<int, int>, int> > vect_state;\
MTK_SUBVARLIST(entries, S2_state, SO3_state) \
name ( \
MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_ARG, entries) \
) : \
MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_COPY, entries) {}\
int getDOF() const { return DOF; } \
void boxplus(const MTK::vectview<const scalar, DOF> & __vec, scalar __scale = 1 ) { \
MTK_TRANSFORM(MTK_BOXPLUS, entries)\
} \
void oplus(const MTK::vectview<const scalar, DIM> & __vec, scalar __scale = 1 ) { \
MTK_TRANSFORM(MTK_OPLUS, entries)\
} \
void boxminus(MTK::vectview<scalar,DOF> __res, const name& __oth) const { \
MTK_TRANSFORM(MTK_BOXMINUS, entries)\
} \
friend std::ostream& operator<<(std::ostream& __os, const name& __var){ \
return __os MTK_TRANSFORM(MTK_OSTREAM, entries); \
} \
void build_S2_state(){\
MTK_TRANSFORM(MTK_S2_state, entries)\
}\
void build_vect_state(){\
MTK_TRANSFORM(MTK_vect_state, entries)\
}\
void build_SO3_state(){\
MTK_TRANSFORM(MTK_SO3_state, entries)\
}\
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res, int idx) {\
MTK_TRANSFORM(MTK_S2_hat, entries)\
}\
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res, int idx) {\
MTK_TRANSFORM(MTK_S2_Nx_yy, entries)\
}\
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, Eigen::Matrix<scalar, 2, 1> dx, int idx) {\
MTK_TRANSFORM(MTK_S2_Mx, entries)\
}\
friend std::istream& operator>>(std::istream& __is, name& __var){ \
return __is MTK_TRANSFORM(MTK_ISTREAM, entries); \
} \
};
#endif /*MTK_AUTOCONSTRUCT_HPP_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/SubManifold.hpp
* @brief Defines the SubManifold class
*/
#ifndef SUBMANIFOLD_HPP_
#define SUBMANIFOLD_HPP_
#include "vectview.hpp"
namespace MTK {
/**
* @ingroup SubManifolds
* Helper class for compound manifolds.
* This class wraps a manifold T and provides an enum IDX refering to the
* index of the SubManifold within the compound manifold.
*
* Memberpointers to a submanifold can be used for @ref SubManifolds "functions accessing submanifolds".
*
* @tparam T The manifold type of the sub-type
* @tparam idx The index of the sub-type within the compound manifold
*/
template<class T, int idx, int dim>
struct SubManifold : public T
{
enum {IDX = idx, DIM = dim /*!< index of the sub-type within the compound manifold */ };
//! manifold type
typedef T type;
//! Construct from derived type
template<class X>
explicit
SubManifold(const X& t) : T(t) {};
//! Construct from internal type
//explicit
SubManifold(const T& t) : T(t) {};
//! inherit assignment operator
using T::operator=;
};
} // namespace MTK
#endif /* SUBMANIFOLD_HPP_ */

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/mtkmath.hpp
* @brief several math utility functions.
*/
#ifndef MTKMATH_H_
#define MTKMATH_H_
#include <cmath>
#include <boost/math/tools/precision.hpp>
#include "../types/vect.hpp"
#ifndef M_PI
#define M_PI 3.1415926535897932384626433832795
#endif
namespace MTK {
namespace internal {
template<class Manifold>
struct traits {
typedef typename Manifold::scalar scalar;
enum {DOF = Manifold::DOF};
typedef vect<DOF, scalar> vectorized_type;
typedef Eigen::Matrix<scalar, DOF, DOF> matrix_type;
};
template<>
struct traits<float> : traits<Scalar<float> > {};
template<>
struct traits<double> : traits<Scalar<double> > {};
} // namespace internal
/**
* \defgroup MTKMath Mathematical helper functions
*/
//@{
//! constant @f$ \pi @f$
const double pi = M_PI;
template<class scalar> inline scalar tolerance();
template<> inline float tolerance<float >() { return 1e-5f; }
template<> inline double tolerance<double>() { return 1e-11; }
/**
* normalize @a x to @f$[-bound, bound] @f$.
*
* result for @f$ x = bound + 2\cdot n\cdot bound @f$ is arbitrary @f$\pm bound @f$.
*/
template<class scalar>
inline scalar normalize(scalar x, scalar bound){ //not used
if(std::fabs(x) <= bound) return x;
int r = (int)(x *(scalar(1.0)/ bound));
return x - ((r + (r>>31) + 1) & ~1)*bound;
}
/**
* Calculate cosine and sinc of sqrt(x2).
* @param x2 the squared angle must be non-negative
* @return a pair containing cos and sinc of sqrt(x2)
*/
template<class scalar>
std::pair<scalar, scalar> cos_sinc_sqrt(const scalar &x2){
using std::sqrt;
using std::cos;
using std::sin;
static scalar const taylor_0_bound = boost::math::tools::epsilon<scalar>();
static scalar const taylor_2_bound = sqrt(taylor_0_bound);
static scalar const taylor_n_bound = sqrt(taylor_2_bound);
assert(x2>=0 && "argument must be non-negative");
// FIXME check if bigger bounds are possible
if(x2>=taylor_n_bound) {
// slow fall-back solution
scalar x = sqrt(x2);
return std::make_pair(cos(x), sin(x)/x); // x is greater than 0.
}
// FIXME Replace by Horner-Scheme (4 instead of 5 FLOP/term, numerically more stable, theoretically cos and sinc can be calculated in parallel using SSE2 mulpd/addpd)
// TODO Find optimal coefficients using Remez algorithm
static scalar const inv[] = {1/3., 1/4., 1/5., 1/6., 1/7., 1/8., 1/9.};
scalar cosi = 1., sinc=1;
scalar term = -1/2. * x2;
for(int i=0; i<3; ++i) {
cosi += term;
term *= inv[2*i];
sinc += term;
term *= -inv[2*i+1] * x2;
}
return std::make_pair(cosi, sinc);
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> hat(const Base& v) {
Eigen::Matrix<typename Base::scalar, 3, 3> res;
res << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
return res;
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_inv_trans(const Base& v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
if(v.norm() > MTK::tolerance<typename Base::scalar>())
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() + 0.5 * hat<Base>(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm();
}
else
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
return res;
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_inv(const Base& v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
if(v.norm() > MTK::tolerance<typename Base::scalar>())
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() - 0.5 * hat<Base>(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm();
}
else
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
return res;
}
template<typename scalar>
Eigen::Matrix<scalar, 2, 3> S2_w_expw_( Eigen::Matrix<scalar, 2, 1> v, scalar length)
{
Eigen::Matrix<scalar, 2, 3> res;
scalar norm = std::sqrt(v[0]*v[0] + v[1]*v[1]);
if(norm < MTK::tolerance<scalar>()){
res = Eigen::Matrix<scalar, 2, 3>::Zero();
res(0, 1) = 1;
res(1, 2) = 1;
res /= length;
}
else{
res << -v[0]*(1/norm-1/std::tan(norm))/std::sin(norm), norm/std::sin(norm), 0,
-v[1]*(1/norm-1/std::tan(norm))/std::sin(norm), 0, norm/std::sin(norm);
res /= length;
}
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_matrix(const Base & v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
double norm = std::sqrt(squaredNorm);
if(norm < MTK::tolerance<typename Base::scalar>()){
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
else{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() + (1 - std::cos(norm)) / squaredNorm * hat(v) + (1 - std::sin(norm) / norm) / squaredNorm * hat(v) * hat(v);
}
return res;
}
template<class scalar, int n>
scalar exp(vectview<scalar, n> result, vectview<const scalar, n> vec, const scalar& scale = 1) {
scalar norm2 = vec.squaredNorm();
std::pair<scalar, scalar> cos_sinc = cos_sinc_sqrt(scale*scale * norm2);
scalar mult = cos_sinc.second * scale;
result = mult * vec;
return cos_sinc.first;
}
/**
* Inverse function to @c exp.
*
* @param result @c vectview to the result
* @param w scalar part of input
* @param vec vector part of input
* @param scale scale result by this value
* @param plus_minus_periodicity if true values @f$[w, vec]@f$ and @f$[-w, -vec]@f$ give the same result
*/
template<class scalar, int n>
void log(vectview<scalar, n> result,
const scalar &w, const vectview<const scalar, n> vec,
const scalar &scale, bool plus_minus_periodicity)
{
// FIXME implement optimized case for vec.squaredNorm() <= tolerance() * (w*w) via Rational Remez approximation ~> only one division
scalar nv = vec.norm();
if(nv < tolerance<scalar>()) {
if(!plus_minus_periodicity && w < 0) {
// find the maximal entry:
int i;
nv = vec.cwiseAbs().maxCoeff(&i);
result = scale * std::atan2(nv, w) * vect<n, scalar>::Unit(i);
return;
}
nv = tolerance<scalar>();
}
scalar s = scale / nv * (plus_minus_periodicity ? std::atan(nv / w) : std::atan2(nv, w) );
result = s * vec;
}
} // namespace MTK
#endif /* MTKMATH_H_ */

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/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/vectview.hpp
* @brief Wrapper class around a pointer used as interface for plain vectors.
*/
#ifndef VECTVIEW_HPP_
#define VECTVIEW_HPP_
#include <Eigen/Core>
namespace MTK {
/**
* A view to a vector.
* Essentially, @c vectview is only a pointer to @c scalar but can be used directly in @c Eigen expressions.
* The dimension of the vector is given as template parameter and type-checked when used in expressions.
* Data has to be modifiable.
*
* @tparam scalar Scalar type of the vector.
* @tparam dim Dimension of the vector.
*
* @todo @c vectview can be replaced by simple inheritance of @c Eigen::Map, as soon as they get const-correct
*/
namespace internal {
template<class Base, class T1, class T2>
struct CovBlock {
typedef typename Eigen::Block<Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF>, T1::DOF, T2::DOF> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF>, T1::DOF, T2::DOF> ConstType;
};
template<class Base, class T1, class T2>
struct CovBlock_ {
typedef typename Eigen::Block<Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM>, T1::DIM, T2::DIM> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM>, T1::DIM, T2::DIM> ConstType;
};
template<typename Base1, typename Base2, typename T1, typename T2>
struct CrossCovBlock {
typedef typename Eigen::Block<Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF>, T1::DOF, T2::DOF> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF>, T1::DOF, T2::DOF> ConstType;
};
template<typename Base1, typename Base2, typename T1, typename T2>
struct CrossCovBlock_ {
typedef typename Eigen::Block<Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM>, T1::DIM, T2::DIM> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM>, T1::DIM, T2::DIM> ConstType;
};
template<class scalar, int dim>
struct VectviewBase {
typedef Eigen::Matrix<scalar, dim, 1> matrix_type;
typedef typename matrix_type::MapType Type;
typedef typename matrix_type::ConstMapType ConstType;
};
template<class T>
struct UnalignedType {
typedef T type;
};
}
template<class scalar, int dim>
class vectview : public internal::VectviewBase<scalar, dim>::Type {
typedef internal::VectviewBase<scalar, dim> VectviewBase;
public:
//! plain matrix type
typedef typename VectviewBase::matrix_type matrix_type;
//! base type
typedef typename VectviewBase::Type base;
//! construct from pointer
explicit
vectview(scalar* data, int dim_=dim) : base(data, dim_) {}
//! construct from plain matrix
vectview(matrix_type& m) : base(m.data(), m.size()) {}
//! construct from another @c vectview
vectview(const vectview &v) : base(v) {}
//! construct from Eigen::Block:
template<class Base>
vectview(Eigen::VectorBlock<Base, dim> block) : base(&block.coeffRef(0), block.size()) {}
template<class Base, bool PacketAccess>
vectview(Eigen::Block<Base, dim, 1, PacketAccess> block) : base(&block.coeffRef(0), block.size()) {}
//! inherit assignment operator
using base::operator=;
//! data pointer
scalar* data() {return const_cast<scalar*>(base::data());}
};
/**
* @c const version of @c vectview.
* Compared to @c Eigen::Map this implementation is const correct, i.e.,
* data will not be modifiable using this view.
*
* @tparam scalar Scalar type of the vector.
* @tparam dim Dimension of the vector.
*
* @sa vectview
*/
template<class scalar, int dim>
class vectview<const scalar, dim> : public internal::VectviewBase<scalar, dim>::ConstType {
typedef internal::VectviewBase<scalar, dim> VectviewBase;
public:
//! plain matrix type
typedef typename VectviewBase::matrix_type matrix_type;
//! base type
typedef typename VectviewBase::ConstType base;
//! construct from const pointer
explicit
vectview(const scalar* data, int dim_ = dim) : base(data, dim_) {}
//! construct from column vector
template<int options>
vectview(const Eigen::Matrix<scalar, dim, 1, options>& m) : base(m.data()) {}
//! construct from row vector
template<int options, int phony>
vectview(const Eigen::Matrix<scalar, 1, dim, options, phony>& m) : base(m.data()) {}
//! construct from another @c vectview
vectview(vectview<scalar, dim> x) : base(x.data()) {}
//! construct from base
vectview(const base &x) : base(x) {}
/**
* Construct from Block
* @todo adapt this, when Block gets const-correct
*/
template<class Base>
vectview(Eigen::VectorBlock<Base, dim> block) : base(&block.coeffRef(0)) {}
template<class Base, bool PacketAccess>
vectview(Eigen::Block<Base, dim, 1, PacketAccess> block) : base(&block.coeffRef(0)) {}
};
} // namespace MTK
#endif /* VECTVIEW_HPP_ */

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/startIdx.hpp
* @brief Tools to access sub-elements of compound manifolds.
*/
#ifndef GET_START_INDEX_H_
#define GET_START_INDEX_H_
#include <Eigen/Core>
#include "src/SubManifold.hpp"
#include "src/vectview.hpp"
namespace MTK {
/**
* \defgroup SubManifolds Accessing Submanifolds
* For compound manifolds constructed using MTK_BUILD_MANIFOLD, member pointers
* can be used to get sub-vectors or matrix-blocks of a corresponding big matrix.
* E.g. for a type @a pose consisting of @a orient and @a trans the member pointers
* @c &pose::orient and @c &pose::trans give all required information and are still
* valid if the base type gets extended or the actual types of @a orient and @a trans
* change (e.g. from 2D to 3D).
*
* @todo Maybe require manifolds to typedef MatrixType and VectorType, etc.
*/
//@{
/**
* Determine the index of a sub-variable within a compound variable.
*/
template<class Base, class T, int idx, int dim>
int getStartIdx( MTK::SubManifold<T, idx, dim> Base::*)
{
return idx;
}
template<class Base, class T, int idx, int dim>
int getStartIdx_( MTK::SubManifold<T, idx, dim> Base::*)
{
return dim;
}
/**
* Determine the degrees of freedom of a sub-variable within a compound variable.
*/
template<class Base, class T, int idx, int dim>
int getDof( MTK::SubManifold<T, idx, dim> Base::*)
{
return T::DOF;
}
template<class Base, class T, int idx, int dim>
int getDim( MTK::SubManifold<T, idx, dim> Base::*)
{
return T::DIM;
}
/**
* set the diagonal elements of a covariance matrix corresponding to a sub-variable
*/
template<class Base, class T, int idx, int dim>
void setDiagonal(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T, idx, dim> Base::*, const typename Base::scalar &val)
{
cov.diagonal().template segment<T::DOF>(idx).setConstant(val);
}
template<class Base, class T, int idx, int dim>
void setDiagonal_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T, idx, dim> Base::*, const typename Base::scalar &val)
{
cov.diagonal().template segment<T::DIM>(dim).setConstant(val);
}
/**
* Get the subblock of corresponding to two members, i.e.
* \code
* Eigen::Matrix<double, Pose::DOF, Pose::DOF> m;
* MTK::subblock(m, &Pose::orient, &Pose::trans) = some_expression;
* MTK::subblock(m, &Pose::trans, &Pose::orient) = some_expression.trans();
* \endcode
* lets you modify mixed covariance entries in a bigger covariance matrix.
*/
template<class Base, class T1, int idx1, int dim1, class T2, int idx2, int dim2>
typename MTK::internal::CovBlock<Base, T1, T2>::Type
subblock(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T1, idx1, dim1> Base::*, MTK::SubManifold<T2, idx2, dim2> Base::*)
{
return cov.template block<T1::DOF, T2::DOF>(idx1, idx2);
}
template<class Base, class T1, int idx1, int dim1, class T2, int idx2, int dim2>
typename MTK::internal::CovBlock_<Base, T1, T2>::Type
subblock_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T1, idx1, dim1> Base::*, MTK::SubManifold<T2, idx2, dim2> Base::*)
{
return cov.template block<T1::DIM, T2::DIM>(dim1, dim2);
}
template<typename Base1, typename Base2, typename T1, typename T2, int idx1, int idx2, int dim1, int dim2>
typename MTK::internal::CrossCovBlock<Base1, Base2, T1, T2>::Type
subblock(Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF> &cov, MTK::SubManifold<T1, idx1, dim1> Base1::*, MTK::SubManifold<T2, idx2, dim2> Base2::*)
{
return cov.template block<T1::DOF, T2::DOF>(idx1, idx2);
}
template<typename Base1, typename Base2, typename T1, typename T2, int idx1, int idx2, int dim1, int dim2>
typename MTK::internal::CrossCovBlock_<Base1, Base2, T1, T2>::Type
subblock_(Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM> &cov, MTK::SubManifold<T1, idx1, dim1> Base1::*, MTK::SubManifold<T2, idx2, dim2> Base2::*)
{
return cov.template block<T1::DIM, T2::DIM>(dim1, dim2);
}
/**
* Get the subblock of corresponding to a member, i.e.
* \code
* Eigen::Matrix<double, Pose::DOF, Pose::DOF> m;
* MTK::subblock(m, &Pose::orient) = some_expression;
* \endcode
* lets you modify covariance entries in a bigger covariance matrix.
*/
template<class Base, class T, int idx, int dim>
typename MTK::internal::CovBlock_<Base, T, T>::Type
subblock_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T, idx, dim> Base::*)
{
return cov.template block<T::DIM, T::DIM>(dim, dim);
}
template<class Base, class T, int idx, int dim>
typename MTK::internal::CovBlock<Base, T, T>::Type
subblock(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T, idx, dim> Base::*)
{
return cov.template block<T::DOF, T::DOF>(idx, idx);
}
template<typename Base>
class get_cov {
public:
typedef Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> type;
typedef const Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> const_type;
};
template<typename Base>
class get_cov_ {
public:
typedef Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> type;
typedef const Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> const_type;
};
template<typename Base1, typename Base2>
class get_cross_cov {
public:
typedef Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF> type;
typedef const type const_type;
};
template<typename Base1, typename Base2>
class get_cross_cov_ {
public:
typedef Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM> type;
typedef const type const_type;
};
template<class Base, class T, int idx, int dim>
vectview<typename Base::scalar, T::DIM>
subvector_impl_(vectview<typename Base::scalar, Base::DIM> vec, SubManifold<T, idx, dim> Base::*)
{
return vec.template segment<T::DIM>(dim);
}
template<class Base, class T, int idx, int dim>
vectview<typename Base::scalar, T::DOF>
subvector_impl(vectview<typename Base::scalar, Base::DOF> vec, SubManifold<T, idx, dim> Base::*)
{
return vec.template segment<T::DOF>(idx);
}
/**
* Get the subvector corresponding to a sub-manifold from a bigger vector.
*/
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<Scalar, T::DIM>
subvector_(vectview<Scalar, BaseDIM> vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vec, ptr);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<Scalar, T::DOF>
subvector(vectview<Scalar, BaseDOF> vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vec, ptr);
}
/**
* @todo This should be covered already by subvector(vectview<typename Base::scalar,Base::DOF> vec,SubManifold<T,idx> Base::*)
*/
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<Scalar, T::DOF>
subvector(Eigen::Matrix<Scalar, BaseDOF, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vectview<Scalar, BaseDOF>(vec), ptr);
}
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<Scalar, T::DIM>
subvector_(Eigen::Matrix<Scalar, BaseDIM, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vectview<Scalar, BaseDIM>(vec), ptr);
}
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DIM>
subvector_(const Eigen::Matrix<Scalar, BaseDIM, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vectview<const Scalar, BaseDIM>(vec), ptr);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DOF>
subvector(const Eigen::Matrix<Scalar, BaseDOF, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vectview<const Scalar, BaseDOF>(vec), ptr);
}
/**
* const version of subvector(vectview<typename Base::scalar,Base::DOF> vec,SubManifold<T,idx> Base::*)
*/
template<class Base, class T, int idx, int dim>
vectview<const typename Base::scalar, T::DOF>
subvector_impl(const vectview<const typename Base::scalar, Base::DOF> cvec, SubManifold<T, idx, dim> Base::*)
{
return cvec.template segment<T::DOF>(idx);
}
template<class Base, class T, int idx, int dim>
vectview<const typename Base::scalar, T::DIM>
subvector_impl_(const vectview<const typename Base::scalar, Base::DIM> cvec, SubManifold<T, idx, dim> Base::*)
{
return cvec.template segment<T::DIM>(dim);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DOF>
subvector(const vectview<const Scalar, BaseDOF> cvec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(cvec, ptr);
}
} // namespace MTK
#endif // GET_START_INDEX_H_

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// This is a NEW implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/S2.hpp
* @brief Unit vectors on the sphere, or directions in 3D.
*/
#ifndef S2_H_
#define S2_H_
#include "vect.hpp"
#include "SOn.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Manifold representation of @f$ S^2 @f$.
* Used for unit vectors on the sphere or directions in 3D.
*
* @todo add conversions from/to polar angles?
*/
template<class _scalar = double, int den = 1, int num = 1, int S2_typ = 3>
struct S2 {
typedef _scalar scalar;
typedef vect<3, scalar> vect_type;
typedef SO3<scalar> SO3_type;
typedef typename vect_type::base vec3;
scalar length = scalar(den)/scalar(num);
enum {DOF=2, TYP = 1, DIM = 3};
//private:
/**
* Unit vector on the sphere, or vector pointing in a direction
*/
vect_type vec;
public:
S2() {
if(S2_typ == 3) vec=length * vec3(0, 0, std::sqrt(1));
if(S2_typ == 2) vec=length * vec3(0, std::sqrt(1), 0);
if(S2_typ == 1) vec=length * vec3(std::sqrt(1), 0, 0);
}
S2(const scalar &x, const scalar &y, const scalar &z) : vec(vec3(x, y, z)) {
vec.normalize();
vec = vec * length;
}
S2(const vect_type &_vec) : vec(_vec) {
vec.normalize();
vec = vec * length;
}
void oplus(MTK::vectview<const scalar, 3> delta, scalar scale = 1)
{
SO3_type res;
res.w() = MTK::exp<scalar, 3>(res.vec(), delta, scalar(scale/2));
vec = res.toRotationMatrix() * vec;
}
void boxplus(MTK::vectview<const scalar, 2> delta, scalar scale=1) {
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
vect_type Bu = Bx*delta;SO3_type res;
res.w() = MTK::exp<scalar, 3>(res.vec(), Bu, scalar(scale/2));
vec = res.toRotationMatrix() * vec;
}
void boxminus(MTK::vectview<scalar, 2> res, const S2<scalar, den, num, S2_typ>& other) const {
scalar v_sin = (MTK::hat(vec)*other.vec).norm();
scalar v_cos = vec.transpose() * other.vec;
scalar theta = std::atan2(v_sin, v_cos);
if(v_sin < MTK::tolerance<scalar>())
{
if(std::fabs(theta) > MTK::tolerance<scalar>() )
{
res[0] = 3.1415926;
res[1] = 0;
}
else{
res[0] = 0;
res[1] = 0;
}
}
else
{
S2<scalar, den, num, S2_typ> other_copy = other;
Eigen::Matrix<scalar, 3, 2>Bx;
other_copy.S2_Bx(Bx);
res = theta/v_sin * Bx.transpose() * MTK::hat(other.vec)*vec;
}
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
Eigen::Matrix<scalar, 3, 3> skew_vec;
skew_vec << scalar(0), -vec[2], vec[1],
vec[2], scalar(0), -vec[0],
-vec[1], vec[0], scalar(0);
res = skew_vec;
}
void S2_Bx(Eigen::Matrix<scalar, 3, 2> &res)
{
if(S2_typ == 3)
{
if(vec[2] + length > tolerance<scalar>())
{
res << length - vec[0]*vec[0]/(length+vec[2]), -vec[0]*vec[1]/(length+vec[2]),
-vec[0]*vec[1]/(length+vec[2]), length-vec[1]*vec[1]/(length+vec[2]),
-vec[0], -vec[1];
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
else if(S2_typ == 2)
{
if(vec[1] + length > tolerance<scalar>())
{
res << length - vec[0]*vec[0]/(length+vec[1]), -vec[0]*vec[2]/(length+vec[1]),
-vec[0], -vec[2],
-vec[0]*vec[2]/(length+vec[1]), length-vec[2]*vec[2]/(length+vec[1]);
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
else
{
if(vec[0] + length > tolerance<scalar>())
{
res << -vec[1], -vec[2],
length - vec[1]*vec[1]/(length+vec[0]), -vec[2]*vec[1]/(length+vec[0]),
-vec[2]*vec[1]/(length+vec[0]), length-vec[2]*vec[2]/(length+vec[0]);
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
}
void S2_Nx(Eigen::Matrix<scalar, 2, 3> &res, S2<scalar, den, num, S2_typ>& subtrahend)
{
if((vec+subtrahend.vec).norm() > tolerance<scalar>())
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
if((vec-subtrahend.vec).norm() > tolerance<scalar>())
{
scalar v_sin = (MTK::hat(vec)*subtrahend.vec).norm();
scalar v_cos = vec.transpose() * subtrahend.vec;
res = Bx.transpose() * (std::atan2(v_sin, v_cos)/v_sin*MTK::hat(vec)+MTK::hat(vec)*subtrahend.vec*((-v_cos/v_sin/v_sin/length/length/length/length+std::atan2(v_sin, v_cos)/v_sin/v_sin/v_sin)*subtrahend.vec.transpose()*MTK::hat(vec)*MTK::hat(vec)-vec.transpose()/length/length/length/length));
}
else
{
res = 1/length/length*Bx.transpose()*MTK::hat(vec);
}
}
else
{
std::cerr << "No N(x, y) for x=-y" << std::endl;
std::exit(100);
}
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
res = 1/length/length*Bx.transpose()*MTK::hat(vec);
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
if(delta.norm() < tolerance<scalar>())
{
res = -MTK::hat(vec)*Bx;
}
else{
vect_type Bu = Bx*delta;
SO3_type exp_delta;
exp_delta.w() = MTK::exp<scalar, 3>(exp_delta.vec(), Bu, scalar(1/2));
res = -exp_delta.toRotationMatrix()*MTK::hat(vec)*MTK::A_matrix(Bu).transpose()*Bx;
}
}
operator const vect_type&() const{
return vec;
}
const vect_type& get_vect() const {
return vec;
}
friend S2<scalar, den, num, S2_typ> operator*(const SO3<scalar>& rot, const S2<scalar, den, num, S2_typ>& dir)
{
S2<scalar, den, num, S2_typ> ret;
ret.vec = rot * dir.vec;
return ret;
}
scalar operator[](int idx) const {return vec[idx]; }
friend std::ostream& operator<<(std::ostream &os, const S2<scalar, den, num, S2_typ>& vec){
return os << vec.vec.transpose() << " ";
}
friend std::istream& operator>>(std::istream &is, S2<scalar, den, num, S2_typ>& vec){
for(int i=0; i<3; ++i)
is >> vec.vec[i];
vec.vec.normalize();
vec.vec = vec.vec * vec.length;
return is;
}
};
} // namespace MTK
#endif /*S2_H_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/SOn.hpp
* @brief Standard Orthogonal Groups i.e.\ rotatation groups.
*/
#ifndef SON_H_
#define SON_H_
#include <Eigen/Geometry>
#include "vect.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Two-dimensional orientations represented as scalar.
* There is no guarantee that the representing scalar is within any interval,
* but the result of boxminus will always have magnitude @f$\le\pi @f$.
*/
template<class _scalar = double, int Options = Eigen::AutoAlign>
struct SO2 : public Eigen::Rotation2D<_scalar> {
enum {DOF = 1, DIM = 2, TYP = 3};
typedef _scalar scalar;
typedef Eigen::Rotation2D<scalar> base;
typedef vect<DIM, scalar, Options> vect_type;
//! Construct from angle
SO2(const scalar& angle = 0) : base(angle) { }
//! Construct from Eigen::Rotation2D
SO2(const base& src) : base(src) {}
/**
* Construct from 2D vector.
* Resulting orientation will rotate the first unit vector to point to vec.
*/
SO2(const vect_type &vec) : base(atan2(vec[1], vec[0])) {};
//! Calculate @c this->inverse() * @c r
SO2 operator%(const base &r) const {
return base::inverse() * r;
}
//! Calculate @c this->inverse() * @c r
template<class Derived>
vect_type operator%(const Eigen::MatrixBase<Derived> &vec) const {
return base::inverse() * vec;
}
//! Calculate @c *this * @c r.inverse()
SO2 operator/(const SO2 &r) const {
return *this * r.inverse();
}
//! Gets the angle as scalar.
operator scalar() const {
return base::angle();
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
//! @name Manifold requirements
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
base::angle() += scale * vec[0];
}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
base::angle() += scale * vec[0];
}
void boxminus(MTK::vectview<scalar, DOF> res, const SO2<scalar>& other) const {
res[0] = MTK::normalize(base::angle() - other.angle(), scalar(MTK::pi));
}
friend std::istream& operator>>(std::istream &is, SO2<scalar>& ang){
return is >> ang.angle();
}
};
/**
* Three-dimensional orientations represented as Quaternion.
* It is assumed that the internal Quaternion always stays normalized,
* should this not be the case, call inherited member function @c normalize().
*/
template<class _scalar = double, int Options = Eigen::AutoAlign>
struct SO3 : public Eigen::Quaternion<_scalar, Options> {
enum {DOF = 3, DIM = 3, TYP = 2};
typedef _scalar scalar;
typedef Eigen::Quaternion<scalar, Options> base;
typedef Eigen::Quaternion<scalar> Quaternion;
typedef vect<DIM, scalar, Options> vect_type;
//! Calculate @c this->inverse() * @c r
template<class OtherDerived> EIGEN_STRONG_INLINE
Quaternion operator%(const Eigen::QuaternionBase<OtherDerived> &r) const {
return base::conjugate() * r;
}
//! Calculate @c this->inverse() * @c r
template<class Derived>
vect_type operator%(const Eigen::MatrixBase<Derived> &vec) const {
return base::conjugate() * vec;
}
//! Calculate @c this * @c r.conjugate()
template<class OtherDerived> EIGEN_STRONG_INLINE
Quaternion operator/(const Eigen::QuaternionBase<OtherDerived> &r) const {
return *this * r.conjugate();
}
/**
* Construct from real part and three imaginary parts.
* Quaternion is normalized after construction.
*/
SO3(const scalar& w, const scalar& x, const scalar& y, const scalar& z) : base(w, x, y, z) {
base::normalize();
}
/**
* Construct from Eigen::Quaternion.
* @note Non-normalized input may result result in spurious behavior.
*/
SO3(const base& src = base::Identity()) : base(src) {}
/**
* Construct from rotation matrix.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
template<class Derived>
SO3(const Eigen::MatrixBase<Derived>& matrix) : base(matrix) {}
/**
* Construct from arbitrary rotation type.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
template<class Derived>
SO3(const Eigen::RotationBase<Derived, 3>& rotation) : base(rotation.derived()) {}
//! @name Manifold requirements
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SO3 delta = exp(vec, scale);
*this = *this * delta;
}
void boxminus(MTK::vectview<scalar, DOF> res, const SO3<scalar>& other) const {
res = SO3::log(other.conjugate() * *this);
}
//}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SO3 delta = exp(vec, scale);
*this = *this * delta;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const SO3<scalar, Options>& q){
return os << q.coeffs().transpose() << " ";
}
friend std::istream& operator>>(std::istream &is, SO3<scalar, Options>& q){
vect<4,scalar> coeffs;
is >> coeffs;
q.coeffs() = coeffs.normalized();
return is;
}
//! @name Helper functions
//{
/**
* Calculate the exponential map. In matrix terms this would correspond
* to the Rodrigues formula.
*/
// FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround
// static SO3 exp(MTK::vectview<const scalar, 3> dvec, scalar scale = 1){
static SO3 exp(const Eigen::Matrix<scalar, 3, 1>& dvec, scalar scale = 1){
SO3 res;
res.w() = MTK::exp<scalar, 3>(res.vec(), dvec, scalar(scale/2));
return res;
}
/**
* Calculate the inverse of @c exp.
* Only guarantees that <code>exp(log(x)) == x </code>
*/
static typename base::Vector3 log(const SO3 &orient){
typename base::Vector3 res;
MTK::log<scalar, 3>(res, orient.w(), orient.vec(), scalar(2), true);
return res;
}
};
namespace internal {
template<class Scalar, int Options>
struct UnalignedType<SO2<Scalar, Options > >{
typedef SO2<Scalar, Options | Eigen::DontAlign> type;
};
template<class Scalar, int Options>
struct UnalignedType<SO3<Scalar, Options > >{
typedef SO3<Scalar, Options | Eigen::DontAlign> type;
};
} // namespace internal
} // namespace MTK
#endif /*SON_H_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/vect.hpp
* @brief Basic vectors interpreted as manifolds.
*
* This file also implements a simple wrapper for matrices, for arbitrary scalars
* and for positive scalars.
*/
#ifndef VECT_H_
#define VECT_H_
#include <iosfwd>
#include <iostream>
#include <vector>
#include "../src/vectview.hpp"
namespace MTK {
static const Eigen::IOFormat IO_no_spaces(Eigen::StreamPrecision, Eigen::DontAlignCols, ",", ",", "", "", "[", "]");
/**
* A simple vector class.
* Implementation is basically a wrapper around Eigen::Matrix with manifold
* requirements added.
*/
template<int D = 3, class _scalar = double, int _Options=Eigen::AutoAlign>
struct vect : public Eigen::Matrix<_scalar, D, 1, _Options> {
typedef Eigen::Matrix<_scalar, D, 1, _Options> base;
enum {DOF = D, DIM = D, TYP = 0};
typedef _scalar scalar;
//using base::operator=;
/** Standard constructor. Sets all values to zero. */
vect(const base &src = base::Zero()) : base(src) {}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE vect(const Eigen::DenseBase<OtherDerived>& other) : base(other) {}
/** Construct from memory. */
vect(const scalar* src, int size = DOF) : base(base::Map(src, size)) { }
void boxplus(MTK::vectview<const scalar, D> vec, scalar scale=1) {
*this += scale * vec;
}
void boxminus(MTK::vectview<scalar, D> res, const vect<D, scalar>& other) const {
res = *this - other;
}
void oplus(MTK::vectview<const scalar, D> vec, scalar scale=1) {
*this += scale * vec;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const vect<D, scalar, _Options>& v){
// Eigen sometimes messes with the streams flags, so output manually:
for(int i=0; i<DOF; ++i)
os << v(i) << " ";
return os;
}
friend std::istream& operator>>(std::istream &is, vect<D, scalar, _Options>& v){
char term=0;
is >> std::ws; // skip whitespace
switch(is.peek()) {
case '(': term=')'; is.ignore(1); break;
case '[': term=']'; is.ignore(1); break;
case '{': term='}'; is.ignore(1); break;
default: break;
}
if(D==Eigen::Dynamic) {
assert(term !=0 && "Dynamic vectors must be embraced");
std::vector<scalar> temp;
while(is.good() && is.peek() != term) {
scalar x;
is >> x;
temp.push_back(x);
if(is.peek()==',') is.ignore(1);
}
v = vect::Map(temp.data(), temp.size());
} else
for(int i=0; i<v.size(); ++i){
is >> v[i];
if(is.peek()==',') { // ignore commas between values
is.ignore(1);
}
}
if(term!=0) {
char x;
is >> x;
if(x!=term) {
is.setstate(is.badbit);
// assert(x==term && "start and end bracket do not match!");
}
}
return is;
}
template<int dim>
vectview<scalar, dim> tail(){
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template tail<dim>();
}
template<int dim>
vectview<const scalar, dim> tail() const{
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template tail<dim>();
}
template<int dim>
vectview<scalar, dim> head(){
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template head<dim>();
}
template<int dim>
vectview<const scalar, dim> head() const{
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template head<dim>();
}
};
/**
* A simple matrix class.
* Implementation is basically a wrapper around Eigen::Matrix with manifold
* requirements added, i.e., matrix is viewed as a plain vector for that.
*/
template<int M, int N, class _scalar = double, int _Options = Eigen::Matrix<_scalar, M, N>::Options>
struct matrix : public Eigen::Matrix<_scalar, M, N, _Options> {
typedef Eigen::Matrix<_scalar, M, N, _Options> base;
enum {DOF = M * N, TYP = 4, DIM=0};
typedef _scalar scalar;
using base::operator=;
/** Standard constructor. Sets all values to zero. */
matrix() {
base::setZero();
}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE matrix(const Eigen::MatrixBase<OtherDerived>& other) : base(other) {}
/** Construct from memory. */
matrix(const scalar* src) : base(src) { }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
*this += scale * base::Map(vec.data());
}
void boxminus(MTK::vectview<scalar, DOF> res, const matrix& other) const {
base::Map(res.data()) = *this - other;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
*this += scale * base::Map(vec.data());
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const matrix<M, N, scalar, _Options>& mat){
for(int i=0; i<DOF; ++i){
os << mat.data()[i] << " ";
}
return os;
}
friend std::istream& operator>>(std::istream &is, matrix<M, N, scalar, _Options>& mat){
for(int i=0; i<DOF; ++i){
is >> mat.data()[i];
}
return is;
}
};// @todo What if M / N = Eigen::Dynamic?
/**
* A simple scalar type.
*/
template<class _scalar = double>
struct Scalar {
enum {DOF = 1, TYP = 5, DIM=0};
typedef _scalar scalar;
scalar value;
Scalar(const scalar& value = scalar(0)) : value(value) {}
operator const scalar&() const { return value; }
operator scalar&() { return value; }
Scalar& operator=(const scalar& val) { value = val; return *this; }
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
value += scale * vec[0];
}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
value += scale * vec[0];
}
void boxminus(MTK::vectview<scalar, DOF> res, const Scalar& other) const {
res[0] = *this - other;
}
};
/**
* Positive scalars.
* Boxplus is implemented using multiplication by @f$x\boxplus\delta = x\cdot\exp(\delta) @f$.
*/
template<class _scalar = double>
struct PositiveScalar {
enum {DOF = 1, TYP = 6, DIM=0};
typedef _scalar scalar;
scalar value;
PositiveScalar(const scalar& value = scalar(1)) : value(value) {
assert(value > scalar(0));
}
operator const scalar&() const { return value; }
PositiveScalar& operator=(const scalar& val) { assert(val>0); value = val; return *this; }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
value *= std::exp(scale * vec[0]);
}
void boxminus(MTK::vectview<scalar, DOF> res, const PositiveScalar& other) const {
res[0] = std::log(*this / other);
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
value *= std::exp(scale * vec[0]);
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::istream& operator>>(std::istream &is, PositiveScalar<scalar>& s){
is >> s.value;
assert(s.value > 0);
return is;
}
};
template<class _scalar = double>
struct Complex : public std::complex<_scalar>{
enum {DOF = 2, TYP = 7, DIM=0};
typedef _scalar scalar;
typedef std::complex<scalar> Base;
Complex(const Base& value) : Base(value) {}
Complex(const scalar& re = 0.0, const scalar& im = 0.0) : Base(re, im) {}
Complex(const MTK::vectview<const scalar, 2> &in) : Base(in[0], in[1]) {}
template<class Derived>
Complex(const Eigen::DenseBase<Derived> &in) : Base(in[0], in[1]) {}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
Base::real() += scale * vec[0];
Base::imag() += scale * vec[1];
};
void boxminus(MTK::vectview<scalar, DOF> res, const Complex& other) const {
Complex diff = *this - other;
res << diff.real(), diff.imag();
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
Base::real() += scale * vec[0];
Base::imag() += scale * vec[1];
};
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
scalar squaredNorm() const {
return std::pow(Base::real(),2) + std::pow(Base::imag(),2);
}
const scalar& operator()(int i) const {
assert(0<=i && i<2 && "Index out of range");
return i==0 ? Base::real() : Base::imag();
}
scalar& operator()(int i){
assert(0<=i && i<2 && "Index out of range");
return i==0 ? Base::real() : Base::imag();
}
};
namespace internal {
template<int dim, class Scalar, int Options>
struct UnalignedType<vect<dim, Scalar, Options > >{
typedef vect<dim, Scalar, Options | Eigen::DontAlign> type;
};
} // namespace internal
} // namespace MTK
#endif /*VECT_H_*/

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/*
* Copyright (c) 2010--2011, Universitaet Bremen and DFKI GmbH
* All rights reserved.
*
* Author: Rene Wagner <rene.wagner@dfki.de>
* Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the DFKI GmbH
* nor the names of its contributors may be used to endorse or
* promote products derived from this software without specific
* prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef WRAPPED_CV_MAT_HPP_
#define WRAPPED_CV_MAT_HPP_
#include <Eigen/Core>
#include <opencv/cv.h>
namespace MTK {
template<class f_type>
struct cv_f_type;
template<>
struct cv_f_type<double>
{
enum {value = CV_64F};
};
template<>
struct cv_f_type<float>
{
enum {value = CV_32F};
};
/**
* cv_mat wraps a CvMat around an Eigen Matrix
*/
template<int rows, int cols, class f_type = double>
class cv_mat : public matrix<rows, cols, f_type, cols==1 ? Eigen::ColMajor : Eigen::RowMajor>
{
typedef matrix<rows, cols, f_type, cols==1 ? Eigen::ColMajor : Eigen::RowMajor> base_type;
enum {type_ = cv_f_type<f_type>::value};
CvMat cv_mat_;
public:
cv_mat()
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
cv_mat(const cv_mat& oth) : base_type(oth)
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
template<class Derived>
cv_mat(const Eigen::MatrixBase<Derived> &value) : base_type(value)
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
template<class Derived>
cv_mat& operator=(const Eigen::MatrixBase<Derived> &value)
{
base_type::operator=(value);
return *this;
}
cv_mat& operator=(const cv_mat& value)
{
base_type::operator=(value);
return *this;
}
// FIXME: Maybe overloading operator& is not a good idea ...
CvMat* operator&()
{
return &cv_mat_;
}
const CvMat* operator&() const
{
return &cv_mat_;
}
};
} // namespace MTK
#endif /* WRAPPED_CV_MAT_HPP_ */

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#ifndef COMMON_LIB_H
#define COMMON_LIB_H
#include <so3_math.h>
#include <Eigen/Eigen>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <fast_lio/Pose6D.h>
#include <sensor_msgs/Imu.h>
#include <nav_msgs/Odometry.h>
#include <tf/transform_broadcaster.h>
#include <eigen_conversions/eigen_msg.h>
using namespace std;
using namespace Eigen;
#define USE_IKFOM
#define PI_M (3.14159265358)
#define G_m_s2 (9.81) // Gravaty const in GuangDong/China
#define DIM_STATE (18) // Dimension of states (Let Dim(SO(3)) = 3)
#define DIM_PROC_N (12) // Dimension of process noise (Let Dim(SO(3)) = 3)
#define CUBE_LEN (6.0)
#define LIDAR_SP_LEN (2)
#define INIT_COV (1)
#define NUM_MATCH_POINTS (5)
#define MAX_MEAS_DIM (10000)
#define VEC_FROM_ARRAY(v) v[0],v[1],v[2]
#define MAT_FROM_ARRAY(v) v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7],v[8]
#define CONSTRAIN(v,min,max) ((v>min)?((v<max)?v:max):min)
#define ARRAY_FROM_EIGEN(mat) mat.data(), mat.data() + mat.rows() * mat.cols()
#define STD_VEC_FROM_EIGEN(mat) vector<decltype(mat)::Scalar> (mat.data(), mat.data() + mat.rows() * mat.cols())
#define DEBUG_FILE_DIR(name) (string(string(ROOT_DIR) + "Log/"+ name))
typedef fast_lio::Pose6D Pose6D;
typedef pcl::PointXYZINormal PointType;
typedef pcl::PointCloud<PointType> PointCloudXYZI;
typedef vector<PointType, Eigen::aligned_allocator<PointType>> PointVector;
typedef Vector3d V3D;
typedef Matrix3d M3D;
typedef Vector3f V3F;
typedef Matrix3f M3F;
#define MD(a,b) Matrix<double, (a), (b)>
#define VD(a) Matrix<double, (a), 1>
#define MF(a,b) Matrix<float, (a), (b)>
#define VF(a) Matrix<float, (a), 1>
M3D Eye3d(M3D::Identity());
M3F Eye3f(M3F::Identity());
V3D Zero3d(0, 0, 0);
V3F Zero3f(0, 0, 0);
struct MeasureGroup // Lidar data and imu dates for the curent process
{
MeasureGroup()
{
lidar_beg_time = 0.0;
this->lidar.reset(new PointCloudXYZI());
};
double lidar_beg_time;
double lidar_end_time;
PointCloudXYZI::Ptr lidar;
deque<sensor_msgs::Imu::ConstPtr> imu;
};
struct StatesGroup
{
StatesGroup() {
this->rot_end = M3D::Identity();
this->pos_end = Zero3d;
this->vel_end = Zero3d;
this->bias_g = Zero3d;
this->bias_a = Zero3d;
this->gravity = Zero3d;
this->cov = MD(DIM_STATE,DIM_STATE)::Identity() * INIT_COV;
this->cov.block<9,9>(9,9) = MD(9,9)::Identity() * 0.00001;
};
StatesGroup(const StatesGroup& b) {
this->rot_end = b.rot_end;
this->pos_end = b.pos_end;
this->vel_end = b.vel_end;
this->bias_g = b.bias_g;
this->bias_a = b.bias_a;
this->gravity = b.gravity;
this->cov = b.cov;
};
StatesGroup& operator=(const StatesGroup& b)
{
this->rot_end = b.rot_end;
this->pos_end = b.pos_end;
this->vel_end = b.vel_end;
this->bias_g = b.bias_g;
this->bias_a = b.bias_a;
this->gravity = b.gravity;
this->cov = b.cov;
return *this;
};
StatesGroup operator+(const Matrix<double, DIM_STATE, 1> &state_add)
{
StatesGroup a;
a.rot_end = this->rot_end * Exp(state_add(0,0), state_add(1,0), state_add(2,0));
a.pos_end = this->pos_end + state_add.block<3,1>(3,0);
a.vel_end = this->vel_end + state_add.block<3,1>(6,0);
a.bias_g = this->bias_g + state_add.block<3,1>(9,0);
a.bias_a = this->bias_a + state_add.block<3,1>(12,0);
a.gravity = this->gravity + state_add.block<3,1>(15,0);
a.cov = this->cov;
return a;
};
StatesGroup& operator+=(const Matrix<double, DIM_STATE, 1> &state_add)
{
this->rot_end = this->rot_end * Exp(state_add(0,0), state_add(1,0), state_add(2,0));
this->pos_end += state_add.block<3,1>(3,0);
this->vel_end += state_add.block<3,1>(6,0);
this->bias_g += state_add.block<3,1>(9,0);
this->bias_a += state_add.block<3,1>(12,0);
this->gravity += state_add.block<3,1>(15,0);
return *this;
};
Matrix<double, DIM_STATE, 1> operator-(const StatesGroup& b)
{
Matrix<double, DIM_STATE, 1> a;
M3D rotd(b.rot_end.transpose() * this->rot_end);
a.block<3,1>(0,0) = Log(rotd);
a.block<3,1>(3,0) = this->pos_end - b.pos_end;
a.block<3,1>(6,0) = this->vel_end - b.vel_end;
a.block<3,1>(9,0) = this->bias_g - b.bias_g;
a.block<3,1>(12,0) = this->bias_a - b.bias_a;
a.block<3,1>(15,0) = this->gravity - b.gravity;
return a;
};
void resetpose()
{
this->rot_end = M3D::Identity();
this->pos_end = Zero3d;
this->vel_end = Zero3d;
}
M3D rot_end; // the estimated attitude (rotation matrix) at the end lidar point
V3D pos_end; // the estimated position at the end lidar point (world frame)
V3D vel_end; // the estimated velocity at the end lidar point (world frame)
V3D bias_g; // gyroscope bias
V3D bias_a; // accelerator bias
V3D gravity; // the estimated gravity acceleration
Matrix<double, DIM_STATE, DIM_STATE> cov; // states covariance
};
template<typename T>
T rad2deg(T radians)
{
return radians * 180.0 / PI_M;
}
template<typename T>
T deg2rad(T degrees)
{
return degrees * PI_M / 180.0;
}
template<typename T>
auto set_pose6d(const double t, const Matrix<T, 3, 1> &a, const Matrix<T, 3, 1> &g, \
const Matrix<T, 3, 1> &v, const Matrix<T, 3, 1> &p, const Matrix<T, 3, 3> &R)
{
Pose6D rot_kp;
rot_kp.offset_time = t;
for (int i = 0; i < 3; i++)
{
rot_kp.acc[i] = a(i);
rot_kp.gyr[i] = g(i);
rot_kp.vel[i] = v(i);
rot_kp.pos[i] = p(i);
for (int j = 0; j < 3; j++) rot_kp.rot[i*3+j] = R(i,j);
}
return move(rot_kp);
}
/* comment
plane equation: Ax + By + Cz + D = 0
convert to: A/D*x + B/D*y + C/D*z = -1
solve: A0*x0 = b0
where A0_i = [x_i, y_i, z_i], x0 = [A/D, B/D, C/D]^T, b0 = [-1, ..., -1]^T
normvec: normalized x0
*/
template<typename T>
bool esti_normvector(Matrix<T, 3, 1> &normvec, const PointVector &point, const T &threshold, const int &point_num)
{
MatrixXf A(point_num, 3);
MatrixXf b(point_num, 1);
b.setOnes();
b *= -1.0f;
for (int j = 0; j < point_num; j++)
{
A(j,0) = point[j].x;
A(j,1) = point[j].y;
A(j,2) = point[j].z;
}
normvec = A.colPivHouseholderQr().solve(b);
for (int j = 0; j < point_num; j++)
{
if (fabs(normvec(0) * point[j].x + normvec(1) * point[j].y + normvec(2) * point[j].z + 1.0f) > threshold)
{
return false;
}
}
normvec.normalize();
return true;
}
float calc_dist(PointType p1, PointType p2){
float d = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) + (p1.z - p2.z) * (p1.z - p2.z);
return d;
}
template<typename T>
bool esti_plane(Matrix<T, 4, 1> &pca_result, const PointVector &point, const T &threshold)
{
Matrix<T, NUM_MATCH_POINTS, 3> A;
Matrix<T, NUM_MATCH_POINTS, 1> b;
A.setZero();
b.setOnes();
b *= -1.0f;
for (int j = 0; j < NUM_MATCH_POINTS; j++)
{
A(j,0) = point[j].x;
A(j,1) = point[j].y;
A(j,2) = point[j].z;
}
Matrix<T, 3, 1> normvec = A.colPivHouseholderQr().solve(b);
T n = normvec.norm();
pca_result(0) = normvec(0) / n;
pca_result(1) = normvec(1) / n;
pca_result(2) = normvec(2) / n;
pca_result(3) = 1.0 / n;
for (int j = 0; j < NUM_MATCH_POINTS; j++)
{
if (fabs(pca_result(0) * point[j].x + pca_result(1) * point[j].y + pca_result(2) * point[j].z + pca_result(3)) > threshold)
{
return false;
}
}
return true;
}
#endif

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# ikd-Tree
ikd-Tree is an incremental k-d tree for robotic applications.

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#pragma once
#include <stdio.h>
#include <queue>
#include <pthread.h>
#include <chrono>
#include <time.h>
#include <unistd.h>
#include <math.h>
#include <algorithm>
#include <memory.h>
#include <pcl/point_types.h>
#define EPSS 1e-6
#define Minimal_Unbalanced_Tree_Size 10
#define Multi_Thread_Rebuild_Point_Num 1500
#define DOWNSAMPLE_SWITCH true
#define ForceRebuildPercentage 0.2
#define Q_LEN 1000000
using namespace std;
// typedef pcl::PointXYZINormal PointType;
// typedef vector<PointType, Eigen::aligned_allocator<PointType>> PointVector;
struct BoxPointType
{
float vertex_min[3];
float vertex_max[3];
};
enum operation_set
{
ADD_POINT,
DELETE_POINT,
DELETE_BOX,
ADD_BOX,
DOWNSAMPLE_DELETE,
PUSH_DOWN
};
enum delete_point_storage_set
{
NOT_RECORD,
DELETE_POINTS_REC,
MULTI_THREAD_REC
};
template <typename PointType>
class KD_TREE
{
// using MANUAL_Q_ = MANUAL_Q<typename PointType>;
// using PointVector = std::vector<PointType>;
// using MANUAL_Q_ = MANUAL_Q<typename PointType>;
public:
using PointVector = std::vector<PointType, Eigen::aligned_allocator<PointType>>;
using Ptr = std::shared_ptr<KD_TREE<PointType>>;
struct KD_TREE_NODE
{
PointType point;
int division_axis;
int TreeSize = 1;
int invalid_point_num = 0;
int down_del_num = 0;
bool point_deleted = false;
bool tree_deleted = false;
bool point_downsample_deleted = false;
bool tree_downsample_deleted = false;
bool need_push_down_to_left = false;
bool need_push_down_to_right = false;
bool working_flag = false;
pthread_mutex_t push_down_mutex_lock;
float node_range_x[2], node_range_y[2], node_range_z[2];
float radius_sq;
KD_TREE_NODE *left_son_ptr = nullptr;
KD_TREE_NODE *right_son_ptr = nullptr;
KD_TREE_NODE *father_ptr = nullptr;
// For paper data record
float alpha_del;
float alpha_bal;
};
struct Operation_Logger_Type
{
PointType point;
BoxPointType boxpoint;
bool tree_deleted, tree_downsample_deleted;
operation_set op;
};
// static const PointType zeroP;
struct PointType_CMP
{
PointType point;
float dist = 0.0;
PointType_CMP(PointType p = PointType(), float d = INFINITY)
{
this->point = p;
this->dist = d;
};
bool operator<(const PointType_CMP &a) const
{
if (fabs(dist - a.dist) < 1e-10)
return point.x < a.point.x;
else
return dist < a.dist;
}
};
class MANUAL_HEAP
{
public:
MANUAL_HEAP(int max_capacity = 100)
{
cap = max_capacity;
heap = new PointType_CMP[max_capacity];
heap_size = 0;
}
~MANUAL_HEAP()
{
delete[] heap;
}
void pop()
{
if (heap_size == 0)
return;
heap[0] = heap[heap_size - 1];
heap_size--;
MoveDown(0);
return;
}
PointType_CMP top()
{
return heap[0];
}
void push(PointType_CMP point)
{
if (heap_size >= cap)
return;
heap[heap_size] = point;
FloatUp(heap_size);
heap_size++;
return;
}
int size()
{
return heap_size;
}
void clear()
{
heap_size = 0;
return;
}
private:
PointType_CMP *heap;
void MoveDown(int heap_index)
{
int l = heap_index * 2 + 1;
PointType_CMP tmp = heap[heap_index];
while (l < heap_size)
{
if (l + 1 < heap_size && heap[l] < heap[l + 1])
l++;
if (tmp < heap[l])
{
heap[heap_index] = heap[l];
heap_index = l;
l = heap_index * 2 + 1;
}
else
break;
}
heap[heap_index] = tmp;
return;
}
void FloatUp(int heap_index)
{
int ancestor = (heap_index - 1) / 2;
PointType_CMP tmp = heap[heap_index];
while (heap_index > 0)
{
if (heap[ancestor] < tmp)
{
heap[heap_index] = heap[ancestor];
heap_index = ancestor;
ancestor = (heap_index - 1) / 2;
}
else
break;
}
heap[heap_index] = tmp;
return;
}
int heap_size = 0;
int cap = 0;
};
class MANUAL_Q
{
private:
int head = 0, tail = 0, counter = 0;
Operation_Logger_Type q[Q_LEN];
bool is_empty;
public:
void pop()
{
if (counter == 0)
return;
head++;
head %= Q_LEN;
counter--;
if (counter == 0)
is_empty = true;
return;
}
Operation_Logger_Type front()
{
return q[head];
}
Operation_Logger_Type back()
{
return q[tail];
}
void clear()
{
head = 0;
tail = 0;
counter = 0;
is_empty = true;
return;
}
void push(Operation_Logger_Type op)
{
q[tail] = op;
counter++;
if (is_empty)
is_empty = false;
tail++;
tail %= Q_LEN;
}
bool empty()
{
return is_empty;
}
int size()
{
return counter;
}
};
private:
// Multi-thread Tree Rebuild
bool termination_flag = false;
bool rebuild_flag = false;
pthread_t rebuild_thread;
pthread_mutex_t termination_flag_mutex_lock, rebuild_ptr_mutex_lock, working_flag_mutex, search_flag_mutex;
pthread_mutex_t rebuild_logger_mutex_lock, points_deleted_rebuild_mutex_lock;
// queue<Operation_Logger_Type> Rebuild_Logger;
MANUAL_Q Rebuild_Logger;
PointVector Rebuild_PCL_Storage;
KD_TREE_NODE **Rebuild_Ptr = nullptr;
int search_mutex_counter = 0;
static void *multi_thread_ptr(void *arg);
void multi_thread_rebuild();
void start_thread();
void stop_thread();
void run_operation(KD_TREE_NODE **root, Operation_Logger_Type operation);
// KD Tree Functions and augmented variables
int Treesize_tmp = 0, Validnum_tmp = 0;
float alpha_bal_tmp = 0.5, alpha_del_tmp = 0.0;
float delete_criterion_param = 0.5f;
float balance_criterion_param = 0.7f;
float downsample_size = 0.2f;
bool Delete_Storage_Disabled = false;
KD_TREE_NODE *STATIC_ROOT_NODE = nullptr;
PointVector Points_deleted;
PointVector Downsample_Storage;
PointVector Multithread_Points_deleted;
void InitTreeNode(KD_TREE_NODE *root);
void Test_Lock_States(KD_TREE_NODE *root);
void BuildTree(KD_TREE_NODE **root, int l, int r, PointVector &Storage);
void Rebuild(KD_TREE_NODE **root);
int Delete_by_range(KD_TREE_NODE **root, BoxPointType boxpoint, bool allow_rebuild, bool is_downsample);
void Delete_by_point(KD_TREE_NODE **root, PointType point, bool allow_rebuild);
void Add_by_point(KD_TREE_NODE **root, PointType point, bool allow_rebuild, int father_axis);
void Add_by_range(KD_TREE_NODE **root, BoxPointType boxpoint, bool allow_rebuild);
void Search(KD_TREE_NODE *root, int k_nearest, PointType point, MANUAL_HEAP &q, float max_dist); //priority_queue<PointType_CMP>
void Search_by_range(KD_TREE_NODE *root, BoxPointType boxpoint, PointVector &Storage);
void Search_by_radius(KD_TREE_NODE *root, PointType point, float radius, PointVector &Storage);
bool Criterion_Check(KD_TREE_NODE *root);
void Push_Down(KD_TREE_NODE *root);
void Update(KD_TREE_NODE *root);
void delete_tree_nodes(KD_TREE_NODE **root);
void downsample(KD_TREE_NODE **root);
bool same_point(PointType a, PointType b);
float calc_dist(PointType a, PointType b);
float calc_box_dist(KD_TREE_NODE *node, PointType point);
static bool point_cmp_x(PointType a, PointType b);
static bool point_cmp_y(PointType a, PointType b);
static bool point_cmp_z(PointType a, PointType b);
public:
KD_TREE(float delete_param = 0.5, float balance_param = 0.6, float box_length = 0.2);
~KD_TREE();
void Set_delete_criterion_param(float delete_param)
{
delete_criterion_param = delete_param;
}
void Set_balance_criterion_param(float balance_param)
{
balance_criterion_param = balance_param;
}
void set_downsample_param(float downsample_param)
{
downsample_size = downsample_param;
}
void InitializeKDTree(float delete_param = 0.5, float balance_param = 0.7, float box_length = 0.2);
int size();
int validnum();
void root_alpha(float &alpha_bal, float &alpha_del);
void Build(PointVector point_cloud);
void Nearest_Search(PointType point, int k_nearest, PointVector &Nearest_Points, vector<float> &Point_Distance, float max_dist = INFINITY);
void Box_Search(const BoxPointType &Box_of_Point, PointVector &Storage);
void Radius_Search(PointType point, const float radius, PointVector &Storage);
int Add_Points(PointVector &PointToAdd, bool downsample_on);
void Add_Point_Boxes(vector<BoxPointType> &BoxPoints);
void Delete_Points(PointVector &PointToDel);
int Delete_Point_Boxes(vector<BoxPointType> &BoxPoints);
void flatten(KD_TREE_NODE *root, PointVector &Storage, delete_point_storage_set storage_type);
void acquire_removed_points(PointVector &removed_points);
BoxPointType tree_range();
PointVector PCL_Storage;
KD_TREE_NODE *Root_Node = nullptr;
int max_queue_size = 0;
};
// template <typename PointType>
// PointType KD_TREE<PointType>::zeroP = PointType(0,0,0);

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#ifndef SO3_MATH_H
#define SO3_MATH_H
#include <math.h>
#include <Eigen/Core>
#define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0
template<typename T>
Eigen::Matrix<T, 3, 3> skew_sym_mat(const Eigen::Matrix<T, 3, 1> &v)
{
Eigen::Matrix<T, 3, 3> skew_sym_mat;
skew_sym_mat<<0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0;
return skew_sym_mat;
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &&ang)
{
T ang_norm = ang.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang / ang_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
/// Roderigous Tranformation
return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T, typename Ts>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &ang_vel, const Ts &dt)
{
T ang_vel_norm = ang_vel.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_vel_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang_vel / ang_vel_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
T r_ang = ang_vel_norm * dt;
/// Roderigous Tranformation
return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const T &v1, const T &v2, const T &v3)
{
T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3);
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (norm > 0.00001)
{
T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm};
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_ang);
/// Roderigous Tranformation
return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K;
}
else
{
return Eye3;
}
}
/* Logrithm of a Rotation Matrix */
template<typename T>
Eigen::Matrix<T,3,1> Log(const Eigen::Matrix<T, 3, 3> &R)
{
T theta = (R.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (R.trace() - 1));
Eigen::Matrix<T,3,1> K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1));
return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K);
}
template<typename T>
Eigen::Matrix<T, 3, 1> RotMtoEuler(const Eigen::Matrix<T, 3, 3> &rot)
{
T sy = sqrt(rot(0,0)*rot(0,0) + rot(1,0)*rot(1,0));
bool singular = sy < 1e-6;
T x, y, z;
if(!singular)
{
x = atan2(rot(2, 1), rot(2, 2));
y = atan2(-rot(2, 0), sy);
z = atan2(rot(1, 0), rot(0, 0));
}
else
{
x = atan2(-rot(1, 2), rot(1, 1));
y = atan2(-rot(2, 0), sy);
z = 0;
}
Eigen::Matrix<T, 3, 1> ang(x, y, z);
return ang;
}
#endif

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#ifndef USE_IKFOM_H
#define USE_IKFOM_H
#include <IKFoM_toolkit/esekfom/esekfom.hpp>
typedef MTK::vect<3, double> vect3;
typedef MTK::SO3<double> SO3;
typedef MTK::S2<double, 98090, 10000, 1> S2;
typedef MTK::vect<1, double> vect1;
typedef MTK::vect<2, double> vect2;
MTK_BUILD_MANIFOLD(state_ikfom,
((vect3, pos))
((SO3, rot))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
((vect3, vel))
((vect3, bg))
((vect3, ba))
((S2, grav))
);
MTK_BUILD_MANIFOLD(input_ikfom,
((vect3, acc))
((vect3, gyro))
);
MTK_BUILD_MANIFOLD(process_noise_ikfom,
((vect3, ng))
((vect3, na))
((vect3, nbg))
((vect3, nba))
);
MTK::get_cov<process_noise_ikfom>::type process_noise_cov()
{
MTK::get_cov<process_noise_ikfom>::type cov = MTK::get_cov<process_noise_ikfom>::type::Zero();
MTK::setDiagonal<process_noise_ikfom, vect3, 0>(cov, &process_noise_ikfom::ng, 0.0001);// 0.03
MTK::setDiagonal<process_noise_ikfom, vect3, 3>(cov, &process_noise_ikfom::na, 0.0001); // *dt 0.01 0.01 * dt * dt 0.05
MTK::setDiagonal<process_noise_ikfom, vect3, 6>(cov, &process_noise_ikfom::nbg, 0.00001); // *dt 0.00001 0.00001 * dt *dt 0.3 //0.001 0.0001 0.01
MTK::setDiagonal<process_noise_ikfom, vect3, 9>(cov, &process_noise_ikfom::nba, 0.00001); //0.001 0.05 0.0001/out 0.01
return cov;
}
//double L_offset_to_I[3] = {0.04165, 0.02326, -0.0284}; // Avia
//vect3 Lidar_offset_to_IMU(L_offset_to_I, 3);
Eigen::Matrix<double, 24, 1> get_f(state_ikfom &s, const input_ikfom &in)
{
Eigen::Matrix<double, 24, 1> res = Eigen::Matrix<double, 24, 1>::Zero();
vect3 omega;
in.gyro.boxminus(omega, s.bg);
vect3 a_inertial = s.rot * (in.acc-s.ba);
for(int i = 0; i < 3; i++ ){
res(i) = s.vel[i];
res(i + 3) = omega[i];
res(i + 12) = a_inertial[i] + s.grav[i];
}
return res;
}
Eigen::Matrix<double, 24, 23> df_dx(state_ikfom &s, const input_ikfom &in)
{
Eigen::Matrix<double, 24, 23> cov = Eigen::Matrix<double, 24, 23>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
vect3 acc_;
in.acc.boxminus(acc_, s.ba);
vect3 omega;
in.gyro.boxminus(omega, s.bg);
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix()*MTK::hat(acc_);
cov.template block<3, 3>(12, 18) = -s.rot.toRotationMatrix();
Eigen::Matrix<state_ikfom::scalar, 2, 1> vec = Eigen::Matrix<state_ikfom::scalar, 2, 1>::Zero();
Eigen::Matrix<state_ikfom::scalar, 3, 2> grav_matrix;
s.S2_Mx(grav_matrix, vec, 21);
cov.template block<3, 2>(12, 21) = grav_matrix;
cov.template block<3, 3>(3, 15) = -Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, 24, 12> df_dw(state_ikfom &s, const input_ikfom &in)
{
Eigen::Matrix<double, 24, 12> cov = Eigen::Matrix<double, 24, 12>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
cov.template block<3, 3>(3, 0) = -Eigen::Matrix3d::Identity();
cov.template block<3, 3>(15, 6) = Eigen::Matrix3d::Identity();
cov.template block<3, 3>(18, 9) = Eigen::Matrix3d::Identity();
return cov;
}
vect3 SO3ToEuler(const SO3 &orient)
{
Eigen::Matrix<double, 3, 1> _ang;
Eigen::Vector4d q_data = orient.coeffs().transpose();
//scalar w=orient.coeffs[3], x=orient.coeffs[0], y=orient.coeffs[1], z=orient.coeffs[2];
double sqw = q_data[3]*q_data[3];
double sqx = q_data[0]*q_data[0];
double sqy = q_data[1]*q_data[1];
double sqz = q_data[2]*q_data[2];
double unit = sqx + sqy + sqz + sqw; // if normalized is one, otherwise is correction factor
double test = q_data[3]*q_data[1] - q_data[2]*q_data[0];
if (test > 0.49999*unit) { // singularity at north pole
_ang << 2 * std::atan2(q_data[0], q_data[3]), M_PI/2, 0;
double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3};
vect3 euler_ang(temp, 3);
return euler_ang;
}
if (test < -0.49999*unit) { // singularity at south pole
_ang << -2 * std::atan2(q_data[0], q_data[3]), -M_PI/2, 0;
double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3};
vect3 euler_ang(temp, 3);
return euler_ang;
}
_ang <<
std::atan2(2*q_data[0]*q_data[3]+2*q_data[1]*q_data[2] , -sqx - sqy + sqz + sqw),
std::asin (2*test/unit),
std::atan2(2*q_data[2]*q_data[3]+2*q_data[1]*q_data[0] , sqx - sqy - sqz + sqw);
double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3};
vect3 euler_ang(temp, 3);
// euler_ang[0] = roll, euler_ang[1] = pitch, euler_ang[2] = yaw
return euler_ang;
}
#endif

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src/FAST_LIO/launch/Pointcloud2Map.launch Normal file
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<launch>
<node pkg="octomap_server" type = "octomap_server_node" name="octomap_server">
<param name ="resolution" value="0.1" />
<param name = "frame_id" type="str" value="robot_foot_init" />
<param name = "sensor_model/max_range" value="1000.0" />
<param name = "latch" value="true" />
<param name = "pointcloud_max_z" value="1" />
<param name = "pointcloud_min_z" value="0.0" />
<remap from ="cloud_in" to="/cloud_registered" />
</node>
</launch>

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src/FAST_LIO/launch/gdb_debug_example.launch Normal file
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<launch>
<arg name="rviz" default="true" />
<node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" required="true" launch-prefix="gdb -ex run --args">
<param name="imu_topic" type="string" value="/livox/imu" />
<param name="map_file_path" type="string" value=" " />
<param name="max_iteration" type="int" value="4" />
<param name="dense_map_enable" type="bool" value="1" />
<param name="fov_degree" type="double" value="75" />
<param name="filter_size_corner" type="double" value="0.2" />
<param name="filter_size_surf" type="double" value="0.2" />
<param name="filter_size_map" type="double" value="0.5" />
<param name="runtime_pos_log_enable" type="bool" value="1" />
<param name="cube_side_length" type="double" value="2000" />
</node>
<!-- <group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
</group> -->
</launch>

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src/FAST_LIO/launch/mapping_avia.launch Normal file
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<launch>
<!-- Launch file for Livox AVIA LiDAR -->
<arg name="rviz" default="true" />
<rosparam command="load" file="$(find fast_lio)/config/avia.yaml" />
<param name="feature_extract_enable" type="bool" value="0"/>
<param name="point_filter_num" type="int" value="3"/>
<param name="max_iteration" type="int" value="3" />
<param name="filter_size_surf" type="double" value="0.5" />
<param name="filter_size_map" type="double" value="0.5" />
<param name="cube_side_length" type="double" value="1000" />
<param name="runtime_pos_log_enable" type="bool" value="0" />
<node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" />
<group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
</group>
</launch>

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src/FAST_LIO/launch/mapping_horizon.launch Normal file
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<launch>
<!-- Launch file for Livox Horizon LiDAR -->
<arg name="rviz" default="true" />
<rosparam command="load" file="$(find fast_lio)/config/horizon.yaml" />
<param name="feature_extract_enable" type="bool" value="0"/>
<param name="point_filter_num" type="int" value="3"/>
<param name="max_iteration" type="int" value="3" />
<param name="filter_size_surf" type="double" value="0.5" />
<param name="filter_size_map" type="double" value="0.5" />
<param name="cube_side_length" type="double" value="1000" />
<param name="runtime_pos_log_enable" type="bool" value="0" />
<node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" />
<group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
</group>
</launch>

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src/FAST_LIO/launch/mapping_mid360.launch Normal file
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<launch>
<!-- Launch file for Livox MID360 LiDAR -->
<arg name="rviz" default="true" />
<arg name="use_sim_time" value="true"/>
<rosparam command="load" file="$(find fast_lio)/config/mid360.yaml" />
<param name="feature_extract_enable" type="bool" value="0"/>
<param name="point_filter_num" type="int" value="3"/>
<param name="max_iteration" type="int" value="3" />
<param name="filter_size_surf" type="double" value="0.5" />
<param name="filter_size_map" type="double" value="0.5" />
<param name="cube_side_length" type="double" value="1000" />
<param name="runtime_pos_log_enable" type="bool" value="0" />
<node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" />
<group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
</group>
<!-- Include child launch file -->
<!-- <include file="$(find fast_lio)/launch/Point2Map.launch">
</include> -->
</launch>

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src/FAST_LIO/launch/mapping_ouster64.launch Normal file
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<launch>
<!-- Launch file for ouster OS2-64 LiDAR -->
<arg name="rviz" default="true" />
<rosparam command="load" file="$(find fast_lio)/config/ouster64.yaml" />
<param name="feature_extract_enable" type="bool" value="0"/>
<param name="point_filter_num" type="int" value="4"/>
<param name="max_iteration" type="int" value="3" />
<param name="filter_size_surf" type="double" value="0.5" />
<param name="filter_size_map" type="double" value="0.5" />
<param name="cube_side_length" type="double" value="1000" />
<param name="runtime_pos_log_enable" type="bool" value="0" />
<node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" />
<group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
</group>
</launch>

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src/FAST_LIO/launch/mapping_velodyne.launch Normal file
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<launch>
<!-- Launch file for velodyne16 VLP-16 LiDAR -->
<arg name="rviz" default="true" />
<rosparam command="load" file="$(find fast_lio)/config/velodyne.yaml" />
<param name="feature_extract_enable" type="bool" value="0"/>
<param name="point_filter_num" type="int" value="4"/>
<param name="max_iteration" type="int" value="3" />
<param name="filter_size_surf" type="double" value="0.5" />
<param name="filter_size_map" type="double" value="0.5" />
<param name="cube_side_length" type="double" value="1000" />
<param name="runtime_pos_log_enable" type="bool" value="0" />
<node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" />
<group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
</group>
</launch>

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src/FAST_LIO/msg/Pose6D.msg Normal file
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# the preintegrated Lidar states at the time of IMU measurements in a frame
float64 offset_time # the offset time of IMU measurement w.r.t the first lidar point
float64[3] acc # the preintegrated total acceleration (global frame) at the Lidar origin
float64[3] gyr # the unbiased angular velocity (body frame) at the Lidar origin
float64[3] vel # the preintegrated velocity (global frame) at the Lidar origin
float64[3] pos # the preintegrated position (global frame) at the Lidar origin
float64[9] rot # the preintegrated rotation (global frame) at the Lidar origin

47
src/FAST_LIO/package.xml Normal file
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<?xml version="1.0"?>
<package>
<name>fast_lio</name>
<version>0.0.0</version>
<description>
This is a modified version of LOAM which is original algorithm
is described in the following paper:
J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time.
Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014.
</description>
<maintainer email="dev@livoxtech.com">claydergc</maintainer>
<license>BSD</license>
<author email="zhangji@cmu.edu">Ji Zhang</author>
<buildtool_depend>catkin</buildtool_depend>
<build_depend>geometry_msgs</build_depend>
<build_depend>nav_msgs</build_depend>
<build_depend>roscpp</build_depend>
<build_depend>rospy</build_depend>
<build_depend>std_msgs</build_depend>
<build_depend>sensor_msgs</build_depend>
<build_depend>tf</build_depend>
<build_depend>pcl_ros</build_depend>
<build_depend>livox_ros_driver2</build_depend>
<build_depend>message_generation</build_depend>
<run_depend>geometry_msgs</run_depend>
<run_depend>nav_msgs</run_depend>
<run_depend>sensor_msgs</run_depend>
<run_depend>roscpp</run_depend>
<run_depend>rospy</run_depend>
<run_depend>std_msgs</run_depend>
<run_depend>tf</run_depend>
<run_depend>pcl_ros</run_depend>
<run_depend>livox_ros_driver2</run_depend>
<run_depend>message_runtime</run_depend>
<test_depend>rostest</test_depend>
<test_depend>rosbag</test_depend>
<export>
</export>
</package>

0
src/FAST_LIO/rviz_cfg/.gitignore vendored Normal file
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363
src/FAST_LIO/rviz_cfg/loam_livox.rviz Normal file
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Panels:
- Class: rviz/Displays
Help Height: 0
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /mapping1
- /mapping1/surround1
- /mapping1/currPoints1
- /mapping1/currPoints1/Autocompute Value Bounds1
- /Odometry1/Odometry1
- /Odometry1/Odometry1/Shape1
- /Odometry1/Odometry1/Covariance1
- /Odometry1/Odometry1/Covariance1/Position1
- /Odometry1/Odometry1/Covariance1/Orientation1
- /MarkerArray1/Namespaces1
Splitter Ratio: 0.6432291865348816
Tree Height: 451
- Class: rviz/Selection
Name: Selection
- Class: rviz/Tool Properties
Expanded:
- /2D Pose Estimate1
- /2D Nav Goal1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
- Class: rviz/Time
Name: Time
SyncMode: 0
SyncSource: ""
Preferences:
PromptSaveOnExit: true
Toolbars:
toolButtonStyle: 2
Visualization Manager:
Class: ""
Displays:
- Alpha: 1
Cell Size: 1000
Class: rviz/Grid
Color: 160; 160; 164
Enabled: false
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 40
Reference Frame: <Fixed Frame>
Value: false
- Alpha: 1
Class: rviz/Axes
Enabled: false
Length: 0.699999988079071
Name: Axes
Radius: 0.05999999865889549
Reference Frame: <Fixed Frame>
Show Trail: false
Value: false
- Class: rviz/Group
Displays:
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 238; 238; 236
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 238; 238; 236
Name: surround
Position Transformer: XYZ
Queue Size: 1
Selectable: false
Size (Pixels): 1
Size (m): 0.05000000074505806
Style: Points
Topic: /cloud_registered
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 0.10000000149011612
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 15
Min Value: -5
Value: false
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 1000
Enabled: true
Invert Rainbow: true
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: currPoints
Position Transformer: XYZ
Queue Size: 100000
Selectable: true
Size (Pixels): 1
Size (m): 0.009999999776482582
Style: Points
Topic: /cloud_registered
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 0; 0
Color Transformer: FlatColor
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: PointCloud2
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.10000000149011612
Style: Flat Squares
Topic: /Laser_map
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
Enabled: true
Name: mapping
- Class: rviz/Group
Displays:
- Angle Tolerance: 0.009999999776482582
Class: rviz/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: true
Enabled: true
Keep: 1
Name: Odometry
Position Tolerance: 0.0010000000474974513
Queue Size: 10
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.20000000298023224
Color: 255; 85; 0
Head Length: 0
Head Radius: 0
Shaft Length: 0.05000000074505806
Shaft Radius: 0.05000000074505806
Value: Axes
Topic: /Odometry
Unreliable: false
Value: true
Enabled: true
Name: Odometry
- Alpha: 1
Class: rviz/Axes
Enabled: true
Length: 0.699999988079071
Name: Axes
Radius: 0.10000000149011612
Reference Frame: <Fixed Frame>
Show Trail: false
Value: true
- Alpha: 0
Buffer Length: 2
Class: rviz/Path
Color: 25; 255; 255
Enabled: true
Head Diameter: 0
Head Length: 0
Length: 0.30000001192092896
Line Style: Billboards
Line Width: 0.20000000298023224
Name: Path
Offset:
X: 0
Y: 0
Z: 0
Pose Color: 25; 255; 255
Pose Style: None
Queue Size: 10
Radius: 0.029999999329447746
Shaft Diameter: 0.4000000059604645
Shaft Length: 0.4000000059604645
Topic: /path
Unreliable: false
Value: true
- Alpha: 1
Autocompute Intensity Bounds: false
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 239; 41; 41
Max Intensity: 0
Min Color: 239; 41; 41
Min Intensity: 0
Name: PointCloud2
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 4
Size (m): 0.30000001192092896
Style: Spheres
Topic: /cloud_effected
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 13.139549255371094
Min Value: -32.08251953125
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 138; 226; 52
Color Transformer: FlatColor
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 138; 226; 52
Min Color: 138; 226; 52
Name: PointCloud2
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.10000000149011612
Style: Flat Squares
Topic: /Laser_map
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
- Class: rviz/MarkerArray
Enabled: false
Marker Topic: /MarkerArray
Name: MarkerArray
Namespaces:
{}
Queue Size: 100
Value: false
Enabled: true
Global Options:
Background Color: 0; 0; 0
Default Light: true
Fixed Frame: camera_init
Frame Rate: 10
Name: root
Tools:
- Class: rviz/Interact
Hide Inactive Objects: true
- Class: rviz/MoveCamera
- Class: rviz/Select
- Class: rviz/FocusCamera
- Class: rviz/Measure
- Class: rviz/SetInitialPose
Theta std deviation: 0.2617993950843811
Topic: /initialpose
X std deviation: 0.5
Y std deviation: 0.5
- Class: rviz/SetGoal
Topic: /move_base_simple/goal
- Class: rviz/PublishPoint
Single click: true
Topic: /clicked_point
Value: true
Views:
Current:
Class: rviz/Orbit
Distance: 38.82821273803711
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Field of View: 0.7853981852531433
Focal Point:
X: -4.982542037963867
Y: -15.83572006225586
Z: -3.063523054122925
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.35479673743247986
Target Frame: global
Yaw: 1.2271827459335327
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 684
Hide Left Dock: false
Hide Right Dock: true
QMainWindow State: 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
Selection:
collapsed: false
Time:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: true
Width: 1196
X: 35
Y: 23

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#include <cmath>
#include <math.h>
#include <deque>
#include <mutex>
#include <thread>
#include <fstream>
#include <csignal>
#include <ros/ros.h>
#include <so3_math.h>
#include <Eigen/Eigen>
#include <common_lib.h>
#include <pcl/common/io.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <condition_variable>
#include <nav_msgs/Odometry.h>
#include <pcl/common/transforms.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <tf/transform_broadcaster.h>
#include <eigen_conversions/eigen_msg.h>
#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/Imu.h>
#include <sensor_msgs/PointCloud2.h>
#include <geometry_msgs/Vector3.h>
#include "use-ikfom.hpp"
/// *************Preconfiguration
#define MAX_INI_COUNT (10)
const bool time_list(PointType &x, PointType &y) {return (x.curvature < y.curvature);};
/// *************IMU Process and undistortion
class ImuProcess
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
ImuProcess();
~ImuProcess();
void Reset();
void Reset(double start_timestamp, const sensor_msgs::ImuConstPtr &lastimu);
void set_extrinsic(const V3D &transl, const M3D &rot);
void set_extrinsic(const V3D &transl);
void set_extrinsic(const MD(4,4) &T);
void set_gyr_cov(const V3D &scaler);
void set_acc_cov(const V3D &scaler);
void set_gyr_bias_cov(const V3D &b_g);
void set_acc_bias_cov(const V3D &b_a);
Eigen::Matrix<double, 12, 12> Q;
void Process(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI::Ptr pcl_un_);
ofstream fout_imu;
V3D cov_acc;
V3D cov_gyr;
V3D cov_acc_scale;
V3D cov_gyr_scale;
V3D cov_bias_gyr;
V3D cov_bias_acc;
double first_lidar_time;
private:
void IMU_init(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, int &N);
void UndistortPcl(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI &pcl_in_out);
PointCloudXYZI::Ptr cur_pcl_un_;
sensor_msgs::ImuConstPtr last_imu_;
deque<sensor_msgs::ImuConstPtr> v_imu_;
vector<Pose6D> IMUpose;
vector<M3D> v_rot_pcl_;
M3D Lidar_R_wrt_IMU;
V3D Lidar_T_wrt_IMU;
V3D mean_acc;
V3D mean_gyr;
V3D angvel_last;
V3D acc_s_last;
double start_timestamp_;
double last_lidar_end_time_;
int init_iter_num = 1;
bool b_first_frame_ = true;
bool imu_need_init_ = true;
};
ImuProcess::ImuProcess()
: b_first_frame_(true), imu_need_init_(true), start_timestamp_(-1)
{
init_iter_num = 1;
Q = process_noise_cov();
cov_acc = V3D(0.1, 0.1, 0.1);
cov_gyr = V3D(0.1, 0.1, 0.1);
cov_bias_gyr = V3D(0.0001, 0.0001, 0.0001);
cov_bias_acc = V3D(0.0001, 0.0001, 0.0001);
mean_acc = V3D(0, 0, -1.0);
mean_gyr = V3D(0, 0, 0);
angvel_last = Zero3d;
Lidar_T_wrt_IMU = Zero3d;
Lidar_R_wrt_IMU = Eye3d;
last_imu_.reset(new sensor_msgs::Imu());
}
ImuProcess::~ImuProcess() {}
void ImuProcess::Reset()
{
// ROS_WARN("Reset ImuProcess");
mean_acc = V3D(0, 0, -1.0);
mean_gyr = V3D(0, 0, 0);
angvel_last = Zero3d;
imu_need_init_ = true;
start_timestamp_ = -1;
init_iter_num = 1;
v_imu_.clear();
IMUpose.clear();
last_imu_.reset(new sensor_msgs::Imu());
cur_pcl_un_.reset(new PointCloudXYZI());
}
void ImuProcess::set_extrinsic(const MD(4,4) &T)
{
Lidar_T_wrt_IMU = T.block<3,1>(0,3);
Lidar_R_wrt_IMU = T.block<3,3>(0,0);
}
void ImuProcess::set_extrinsic(const V3D &transl)
{
Lidar_T_wrt_IMU = transl;
Lidar_R_wrt_IMU.setIdentity();
}
void ImuProcess::set_extrinsic(const V3D &transl, const M3D &rot)
{
Lidar_T_wrt_IMU = transl;
Lidar_R_wrt_IMU = rot;
}
void ImuProcess::set_gyr_cov(const V3D &scaler)
{
cov_gyr_scale = scaler;
}
void ImuProcess::set_acc_cov(const V3D &scaler)
{
cov_acc_scale = scaler;
}
void ImuProcess::set_gyr_bias_cov(const V3D &b_g)
{
cov_bias_gyr = b_g;
}
void ImuProcess::set_acc_bias_cov(const V3D &b_a)
{
cov_bias_acc = b_a;
}
void ImuProcess::IMU_init(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, int &N)
{
/** 1. initializing the gravity, gyro bias, acc and gyro covariance
** 2. normalize the acceleration measurenments to unit gravity **/
V3D cur_acc, cur_gyr;
if (b_first_frame_)
{
Reset();
N = 1;
b_first_frame_ = false;
const auto &imu_acc = meas.imu.front()->linear_acceleration;
const auto &gyr_acc = meas.imu.front()->angular_velocity;
mean_acc << imu_acc.x, imu_acc.y, imu_acc.z;
mean_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z;
first_lidar_time = meas.lidar_beg_time;
}
for (const auto &imu : meas.imu)
{
const auto &imu_acc = imu->linear_acceleration;
const auto &gyr_acc = imu->angular_velocity;
cur_acc << imu_acc.x, imu_acc.y, imu_acc.z;
cur_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z;
mean_acc += (cur_acc - mean_acc) / N;
mean_gyr += (cur_gyr - mean_gyr) / N;
cov_acc = cov_acc * (N - 1.0) / N + (cur_acc - mean_acc).cwiseProduct(cur_acc - mean_acc) * (N - 1.0) / (N * N);
cov_gyr = cov_gyr * (N - 1.0) / N + (cur_gyr - mean_gyr).cwiseProduct(cur_gyr - mean_gyr) * (N - 1.0) / (N * N);
// cout<<"acc norm: "<<cur_acc.norm()<<" "<<mean_acc.norm()<<endl;
N ++;
}
state_ikfom init_state = kf_state.get_x();
init_state.grav = S2(- mean_acc / mean_acc.norm() * G_m_s2);
//state_inout.rot = Eye3d; // Exp(mean_acc.cross(V3D(0, 0, -1 / scale_gravity)));
init_state.bg = mean_gyr;
init_state.offset_T_L_I = Lidar_T_wrt_IMU;
init_state.offset_R_L_I = Lidar_R_wrt_IMU;
kf_state.change_x(init_state);
esekfom::esekf<state_ikfom, 12, input_ikfom>::cov init_P = kf_state.get_P();
init_P.setIdentity();
init_P(6,6) = init_P(7,7) = init_P(8,8) = 0.00001;
init_P(9,9) = init_P(10,10) = init_P(11,11) = 0.00001;
init_P(15,15) = init_P(16,16) = init_P(17,17) = 0.0001;
init_P(18,18) = init_P(19,19) = init_P(20,20) = 0.001;
init_P(21,21) = init_P(22,22) = 0.00001;
kf_state.change_P(init_P);
last_imu_ = meas.imu.back();
}
void ImuProcess::UndistortPcl(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI &pcl_out)
{
/*** add the imu of the last frame-tail to the of current frame-head ***/
auto v_imu = meas.imu;
v_imu.push_front(last_imu_);
const double &imu_beg_time = v_imu.front()->header.stamp.toSec();
const double &imu_end_time = v_imu.back()->header.stamp.toSec();
const double &pcl_beg_time = meas.lidar_beg_time;
const double &pcl_end_time = meas.lidar_end_time;
/*** sort point clouds by offset time ***/
pcl_out = *(meas.lidar);
sort(pcl_out.points.begin(), pcl_out.points.end(), time_list);
// cout<<"[ IMU Process ]: Process lidar from "<<pcl_beg_time<<" to "<<pcl_end_time<<", " \
// <<meas.imu.size()<<" imu msgs from "<<imu_beg_time<<" to "<<imu_end_time<<endl;
/*** Initialize IMU pose ***/
state_ikfom imu_state = kf_state.get_x();
IMUpose.clear();
IMUpose.push_back(set_pose6d(0.0, acc_s_last, angvel_last, imu_state.vel, imu_state.pos, imu_state.rot.toRotationMatrix()));
/*** forward propagation at each imu point ***/
V3D angvel_avr, acc_avr, acc_imu, vel_imu, pos_imu;
M3D R_imu;
double dt = 0;
input_ikfom in;
for (auto it_imu = v_imu.begin(); it_imu < (v_imu.end() - 1); it_imu++)
{
auto &&head = *(it_imu);
auto &&tail = *(it_imu + 1);
if (tail->header.stamp.toSec() < last_lidar_end_time_) continue;
angvel_avr<<0.5 * (head->angular_velocity.x + tail->angular_velocity.x),
0.5 * (head->angular_velocity.y + tail->angular_velocity.y),
0.5 * (head->angular_velocity.z + tail->angular_velocity.z);
acc_avr <<0.5 * (head->linear_acceleration.x + tail->linear_acceleration.x),
0.5 * (head->linear_acceleration.y + tail->linear_acceleration.y),
0.5 * (head->linear_acceleration.z + tail->linear_acceleration.z);
// fout_imu << setw(10) << head->header.stamp.toSec() - first_lidar_time << " " << angvel_avr.transpose() << " " << acc_avr.transpose() << endl;
acc_avr = acc_avr * G_m_s2 / mean_acc.norm(); // - state_inout.ba;
if(head->header.stamp.toSec() < last_lidar_end_time_)
{
dt = tail->header.stamp.toSec() - last_lidar_end_time_;
// dt = tail->header.stamp.toSec() - pcl_beg_time;
}
else
{
dt = tail->header.stamp.toSec() - head->header.stamp.toSec();
}
in.acc = acc_avr;
in.gyro = angvel_avr;
Q.block<3, 3>(0, 0).diagonal() = cov_gyr;
Q.block<3, 3>(3, 3).diagonal() = cov_acc;
Q.block<3, 3>(6, 6).diagonal() = cov_bias_gyr;
Q.block<3, 3>(9, 9).diagonal() = cov_bias_acc;
kf_state.predict(dt, Q, in);
/* save the poses at each IMU measurements */
imu_state = kf_state.get_x();
angvel_last = angvel_avr - imu_state.bg;
acc_s_last = imu_state.rot * (acc_avr - imu_state.ba);
for(int i=0; i<3; i++)
{
acc_s_last[i] += imu_state.grav[i];
}
double &&offs_t = tail->header.stamp.toSec() - pcl_beg_time;
IMUpose.push_back(set_pose6d(offs_t, acc_s_last, angvel_last, imu_state.vel, imu_state.pos, imu_state.rot.toRotationMatrix()));
}
/*** calculated the pos and attitude prediction at the frame-end ***/
double note = pcl_end_time > imu_end_time ? 1.0 : -1.0;
dt = note * (pcl_end_time - imu_end_time);
kf_state.predict(dt, Q, in);
imu_state = kf_state.get_x();
last_imu_ = meas.imu.back();
last_lidar_end_time_ = pcl_end_time;
/*** undistort each lidar point (backward propagation) ***/
if (pcl_out.points.begin() == pcl_out.points.end()) return;
auto it_pcl = pcl_out.points.end() - 1;
for (auto it_kp = IMUpose.end() - 1; it_kp != IMUpose.begin(); it_kp--)
{
auto head = it_kp - 1;
auto tail = it_kp;
R_imu<<MAT_FROM_ARRAY(head->rot);
// cout<<"head imu acc: "<<acc_imu.transpose()<<endl;
vel_imu<<VEC_FROM_ARRAY(head->vel);
pos_imu<<VEC_FROM_ARRAY(head->pos);
acc_imu<<VEC_FROM_ARRAY(tail->acc);
angvel_avr<<VEC_FROM_ARRAY(tail->gyr);
for(; it_pcl->curvature / double(1000) > head->offset_time; it_pcl --)
{
dt = it_pcl->curvature / double(1000) - head->offset_time;
/* Transform to the 'end' frame, using only the rotation
* Note: Compensation direction is INVERSE of Frame's moving direction
* So if we want to compensate a point at timestamp-i to the frame-e
* P_compensate = R_imu_e ^ T * (R_i * P_i + T_ei) where T_ei is represented in global frame */
M3D R_i(R_imu * Exp(angvel_avr, dt));
V3D P_i(it_pcl->x, it_pcl->y, it_pcl->z);
V3D T_ei(pos_imu + vel_imu * dt + 0.5 * acc_imu * dt * dt - imu_state.pos);
V3D P_compensate = imu_state.offset_R_L_I.conjugate() * (imu_state.rot.conjugate() * (R_i * (imu_state.offset_R_L_I * P_i + imu_state.offset_T_L_I) + T_ei) - imu_state.offset_T_L_I);// not accurate!
// save Undistorted points and their rotation
it_pcl->x = P_compensate(0);
it_pcl->y = P_compensate(1);
it_pcl->z = P_compensate(2);
if (it_pcl == pcl_out.points.begin()) break;
}
}
}
void ImuProcess::Process(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI::Ptr cur_pcl_un_)
{
double t1,t2,t3;
t1 = omp_get_wtime();
if(meas.imu.empty()) {return;};
ROS_ASSERT(meas.lidar != nullptr);
if (imu_need_init_)
{
/// The very first lidar frame
IMU_init(meas, kf_state, init_iter_num);
imu_need_init_ = true;
last_imu_ = meas.imu.back();
state_ikfom imu_state = kf_state.get_x();
if (init_iter_num > MAX_INI_COUNT)
{
cov_acc *= pow(G_m_s2 / mean_acc.norm(), 2);
imu_need_init_ = false;
cov_acc = cov_acc_scale;
cov_gyr = cov_gyr_scale;
ROS_INFO("IMU Initial Done");
// ROS_INFO("IMU Initial Done: Gravity: %.4f %.4f %.4f %.4f; state.bias_g: %.4f %.4f %.4f; acc covarience: %.8f %.8f %.8f; gry covarience: %.8f %.8f %.8f",\
// imu_state.grav[0], imu_state.grav[1], imu_state.grav[2], mean_acc.norm(), cov_bias_gyr[0], cov_bias_gyr[1], cov_bias_gyr[2], cov_acc[0], cov_acc[1], cov_acc[2], cov_gyr[0], cov_gyr[1], cov_gyr[2]);
fout_imu.open(DEBUG_FILE_DIR("imu.txt"),ios::out);
}
return;
}
UndistortPcl(meas, kf_state, *cur_pcl_un_);
t2 = omp_get_wtime();
t3 = omp_get_wtime();
// cout<<"[ IMU Process ]: Time: "<<t3 - t1<<endl;
}

1048
src/FAST_LIO/src/laserMapping.cpp Normal file
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928
src/FAST_LIO/src/preprocess.cpp Normal file
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#include "preprocess.h"
#define RETURN0 0x00
#define RETURN0AND1 0x10
Preprocess::Preprocess()
:feature_enabled(0), lidar_type(AVIA), blind(0.01), point_filter_num(1)
{
inf_bound = 10;
N_SCANS = 6;
SCAN_RATE = 10;
group_size = 8;
disA = 0.01;
disA = 0.1; // B?
p2l_ratio = 225;
limit_maxmid =6.25;
limit_midmin =6.25;
limit_maxmin = 3.24;
jump_up_limit = 170.0;
jump_down_limit = 8.0;
cos160 = 160.0;
edgea = 2;
edgeb = 0.1;
smallp_intersect = 172.5;
smallp_ratio = 1.2;
given_offset_time = false;
jump_up_limit = cos(jump_up_limit/180*M_PI);
jump_down_limit = cos(jump_down_limit/180*M_PI);
cos160 = cos(cos160/180*M_PI);
smallp_intersect = cos(smallp_intersect/180*M_PI);
}
Preprocess::~Preprocess() {}
void Preprocess::set(bool feat_en, int lid_type, double bld, int pfilt_num)
{
feature_enabled = feat_en;
lidar_type = lid_type;
blind = bld;
point_filter_num = pfilt_num;
}
void Preprocess::process(const livox_ros_driver2::CustomMsg::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out)
{
avia_handler(msg);
*pcl_out = pl_surf;
}
void Preprocess::process(const sensor_msgs::PointCloud2::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out)
{
switch (time_unit)
{
case SEC:
time_unit_scale = 1.e3f;
break;
case MS:
time_unit_scale = 1.f;
break;
case US:
time_unit_scale = 1.e-3f;
break;
case NS:
time_unit_scale = 1.e-6f;
break;
default:
time_unit_scale = 1.f;
break;
}
switch (lidar_type)
{
case OUST64:
oust64_handler(msg);
break;
case VELO16:
velodyne_handler(msg);
break;
default:
printf("Error LiDAR Type");
break;
}
*pcl_out = pl_surf;
}
void Preprocess::avia_handler(const livox_ros_driver2::CustomMsg::ConstPtr &msg)
{
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
double t1 = omp_get_wtime();
int plsize = msg->point_num;
// cout<<"plsie: "<<plsize<<endl;
pl_corn.reserve(plsize);
pl_surf.reserve(plsize);
pl_full.resize(plsize);
for(int i=0; i<N_SCANS; i++)
{
pl_buff[i].clear();
pl_buff[i].reserve(plsize);
}
uint valid_num = 0;
if (feature_enabled)
{
for(uint i=1; i<plsize; i++)
{
if((msg->points[i].line < N_SCANS) && ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00))
{
pl_full[i].x = msg->points[i].x;
pl_full[i].y = msg->points[i].y;
pl_full[i].z = msg->points[i].z;
pl_full[i].intensity = msg->points[i].reflectivity;
pl_full[i].curvature = msg->points[i].offset_time / float(1000000); //use curvature as time of each laser points
bool is_new = false;
if((abs(pl_full[i].x - pl_full[i-1].x) > 1e-7)
|| (abs(pl_full[i].y - pl_full[i-1].y) > 1e-7)
|| (abs(pl_full[i].z - pl_full[i-1].z) > 1e-7))
{
pl_buff[msg->points[i].line].push_back(pl_full[i]);
}
}
}
static int count = 0;
static double time = 0.0;
count ++;
double t0 = omp_get_wtime();
for(int j=0; j<N_SCANS; j++)
{
if(pl_buff[j].size() <= 5) continue;
pcl::PointCloud<PointType> &pl = pl_buff[j];
plsize = pl.size();
vector<orgtype> &types = typess[j];
types.clear();
types.resize(plsize);
plsize--;
for(uint i=0; i<plsize; i++)
{
types[i].range = sqrt(pl[i].x * pl[i].x + pl[i].y * pl[i].y);
vx = pl[i].x - pl[i + 1].x;
vy = pl[i].y - pl[i + 1].y;
vz = pl[i].z - pl[i + 1].z;
types[i].dista = sqrt(vx * vx + vy * vy + vz * vz);
}
types[plsize].range = sqrt(pl[plsize].x * pl[plsize].x + pl[plsize].y * pl[plsize].y);
give_feature(pl, types);
// pl_surf += pl;
}
time += omp_get_wtime() - t0;
printf("Feature extraction time: %lf \n", time / count);
}
else
{
for(uint i=1; i<plsize; i++)
{
if((msg->points[i].line < N_SCANS) && ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00))
{
valid_num ++;
if (valid_num % point_filter_num == 0)
{
pl_full[i].x = msg->points[i].x;
pl_full[i].y = msg->points[i].y;
pl_full[i].z = msg->points[i].z;
pl_full[i].intensity = msg->points[i].reflectivity;
pl_full[i].curvature = msg->points[i].offset_time / float(1000000); // use curvature as time of each laser points, curvature unit: ms
if(((abs(pl_full[i].x - pl_full[i-1].x) > 1e-7)
|| (abs(pl_full[i].y - pl_full[i-1].y) > 1e-7)
|| (abs(pl_full[i].z - pl_full[i-1].z) > 1e-7))
&& (pl_full[i].x * pl_full[i].x + pl_full[i].y * pl_full[i].y + pl_full[i].z * pl_full[i].z > (blind * blind)))
{
pl_surf.push_back(pl_full[i]);
}
}
}
}
}
}
void Preprocess::oust64_handler(const sensor_msgs::PointCloud2::ConstPtr &msg)
{
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
pcl::PointCloud<ouster_ros::Point> pl_orig;
pcl::fromROSMsg(*msg, pl_orig);
int plsize = pl_orig.size();
pl_corn.reserve(plsize);
pl_surf.reserve(plsize);
if (feature_enabled)
{
for (int i = 0; i < N_SCANS; i++)
{
pl_buff[i].clear();
pl_buff[i].reserve(plsize);
}
for (uint i = 0; i < plsize; i++)
{
double range = pl_orig.points[i].x * pl_orig.points[i].x + pl_orig.points[i].y * pl_orig.points[i].y + pl_orig.points[i].z * pl_orig.points[i].z;
if (range < (blind * blind)) continue;
Eigen::Vector3d pt_vec;
PointType added_pt;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.3;
if (yaw_angle >= 180.0)
yaw_angle -= 360.0;
if (yaw_angle <= -180.0)
yaw_angle += 360.0;
added_pt.curvature = pl_orig.points[i].t * time_unit_scale;
if(pl_orig.points[i].ring < N_SCANS)
{
pl_buff[pl_orig.points[i].ring].push_back(added_pt);
}
}
for (int j = 0; j < N_SCANS; j++)
{
PointCloudXYZI &pl = pl_buff[j];
int linesize = pl.size();
vector<orgtype> &types = typess[j];
types.clear();
types.resize(linesize);
linesize--;
for (uint i = 0; i < linesize; i++)
{
types[i].range = sqrt(pl[i].x * pl[i].x + pl[i].y * pl[i].y);
vx = pl[i].x - pl[i + 1].x;
vy = pl[i].y - pl[i + 1].y;
vz = pl[i].z - pl[i + 1].z;
types[i].dista = vx * vx + vy * vy + vz * vz;
}
types[linesize].range = sqrt(pl[linesize].x * pl[linesize].x + pl[linesize].y * pl[linesize].y);
give_feature(pl, types);
}
}
else
{
double time_stamp = msg->header.stamp.toSec();
// cout << "===================================" << endl;
// printf("Pt size = %d, N_SCANS = %d\r\n", plsize, N_SCANS);
for (int i = 0; i < pl_orig.points.size(); i++)
{
if (i % point_filter_num != 0) continue;
double range = pl_orig.points[i].x * pl_orig.points[i].x + pl_orig.points[i].y * pl_orig.points[i].y + pl_orig.points[i].z * pl_orig.points[i].z;
if (range < (blind * blind)) continue;
Eigen::Vector3d pt_vec;
PointType added_pt;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
added_pt.curvature = pl_orig.points[i].t * time_unit_scale; // curvature unit: ms
pl_surf.points.push_back(added_pt);
}
}
// pub_func(pl_surf, pub_full, msg->header.stamp);
// pub_func(pl_surf, pub_corn, msg->header.stamp);
}
void Preprocess::velodyne_handler(const sensor_msgs::PointCloud2::ConstPtr &msg)
{
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
pcl::PointCloud<velodyne_ros::Point> pl_orig;
pcl::fromROSMsg(*msg, pl_orig);
int plsize = pl_orig.points.size();
if (plsize == 0) return;
pl_surf.reserve(plsize);
/*** These variables only works when no point timestamps given ***/
double omega_l = 0.361 * SCAN_RATE; // scan angular velocity
std::vector<bool> is_first(N_SCANS,true);
std::vector<double> yaw_fp(N_SCANS, 0.0); // yaw of first scan point
std::vector<float> yaw_last(N_SCANS, 0.0); // yaw of last scan point
std::vector<float> time_last(N_SCANS, 0.0); // last offset time
/*****************************************************************/
if (pl_orig.points[plsize - 1].time > 0)
{
given_offset_time = true;
}
else
{
given_offset_time = false;
double yaw_first = atan2(pl_orig.points[0].y, pl_orig.points[0].x) * 57.29578;
double yaw_end = yaw_first;
int layer_first = pl_orig.points[0].ring;
for (uint i = plsize - 1; i > 0; i--)
{
if (pl_orig.points[i].ring == layer_first)
{
yaw_end = atan2(pl_orig.points[i].y, pl_orig.points[i].x) * 57.29578;
break;
}
}
}
if(feature_enabled)
{
for (int i = 0; i < N_SCANS; i++)
{
pl_buff[i].clear();
pl_buff[i].reserve(plsize);
}
for (int i = 0; i < plsize; i++)
{
PointType added_pt;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
int layer = pl_orig.points[i].ring;
if (layer >= N_SCANS) continue;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.curvature = pl_orig.points[i].time * time_unit_scale; // units: ms
if (!given_offset_time)
{
double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.2957;
if (is_first[layer])
{
// printf("layer: %d; is first: %d", layer, is_first[layer]);
yaw_fp[layer]=yaw_angle;
is_first[layer]=false;
added_pt.curvature = 0.0;
yaw_last[layer]=yaw_angle;
time_last[layer]=added_pt.curvature;
continue;
}
if (yaw_angle <= yaw_fp[layer])
{
added_pt.curvature = (yaw_fp[layer]-yaw_angle) / omega_l;
}
else
{
added_pt.curvature = (yaw_fp[layer]-yaw_angle+360.0) / omega_l;
}
if (added_pt.curvature < time_last[layer]) added_pt.curvature+=360.0/omega_l;
yaw_last[layer] = yaw_angle;
time_last[layer]=added_pt.curvature;
}
pl_buff[layer].points.push_back(added_pt);
}
for (int j = 0; j < N_SCANS; j++)
{
PointCloudXYZI &pl = pl_buff[j];
int linesize = pl.size();
if (linesize < 2) continue;
vector<orgtype> &types = typess[j];
types.clear();
types.resize(linesize);
linesize--;
for (uint i = 0; i < linesize; i++)
{
types[i].range = sqrt(pl[i].x * pl[i].x + pl[i].y * pl[i].y);
vx = pl[i].x - pl[i + 1].x;
vy = pl[i].y - pl[i + 1].y;
vz = pl[i].z - pl[i + 1].z;
types[i].dista = vx * vx + vy * vy + vz * vz;
}
types[linesize].range = sqrt(pl[linesize].x * pl[linesize].x + pl[linesize].y * pl[linesize].y);
give_feature(pl, types);
}
}
else
{
for (int i = 0; i < plsize; i++)
{
PointType added_pt;
// cout<<"!!!!!!"<<i<<" "<<plsize<<endl;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.curvature = pl_orig.points[i].time * time_unit_scale; // curvature unit: ms // cout<<added_pt.curvature<<endl;
if (!given_offset_time)
{
int layer = pl_orig.points[i].ring;
double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.2957;
if (is_first[layer])
{
// printf("layer: %d; is first: %d", layer, is_first[layer]);
yaw_fp[layer]=yaw_angle;
is_first[layer]=false;
added_pt.curvature = 0.0;
yaw_last[layer]=yaw_angle;
time_last[layer]=added_pt.curvature;
continue;
}
// compute offset time
if (yaw_angle <= yaw_fp[layer])
{
added_pt.curvature = (yaw_fp[layer]-yaw_angle) / omega_l;
}
else
{
added_pt.curvature = (yaw_fp[layer]-yaw_angle+360.0) / omega_l;
}
if (added_pt.curvature < time_last[layer]) added_pt.curvature+=360.0/omega_l;
yaw_last[layer] = yaw_angle;
time_last[layer]=added_pt.curvature;
}
if (i % point_filter_num == 0)
{
if(added_pt.x*added_pt.x+added_pt.y*added_pt.y+added_pt.z*added_pt.z > (blind * blind))
{
pl_surf.points.push_back(added_pt);
}
}
}
}
}
void Preprocess::give_feature(pcl::PointCloud<PointType> &pl, vector<orgtype> &types)
{
int plsize = pl.size();
int plsize2;
if(plsize == 0)
{
printf("something wrong\n");
return;
}
uint head = 0;
while(types[head].range < blind)
{
head++;
}
// Surf
plsize2 = (plsize > group_size) ? (plsize - group_size) : 0;
Eigen::Vector3d curr_direct(Eigen::Vector3d::Zero());
Eigen::Vector3d last_direct(Eigen::Vector3d::Zero());
uint i_nex = 0, i2;
uint last_i = 0; uint last_i_nex = 0;
int last_state = 0;
int plane_type;
for(uint i=head; i<plsize2; i++)
{
if(types[i].range < blind)
{
continue;
}
i2 = i;
plane_type = plane_judge(pl, types, i, i_nex, curr_direct);
if(plane_type == 1)
{
for(uint j=i; j<=i_nex; j++)
{
if(j!=i && j!=i_nex)
{
types[j].ftype = Real_Plane;
}
else
{
types[j].ftype = Poss_Plane;
}
}
// if(last_state==1 && fabs(last_direct.sum())>0.5)
if(last_state==1 && last_direct.norm()>0.1)
{
double mod = last_direct.transpose() * curr_direct;
if(mod>-0.707 && mod<0.707)
{
types[i].ftype = Edge_Plane;
}
else
{
types[i].ftype = Real_Plane;
}
}
i = i_nex - 1;
last_state = 1;
}
else // if(plane_type == 2)
{
i = i_nex;
last_state = 0;
}
// else if(plane_type == 0)
// {
// if(last_state == 1)
// {
// uint i_nex_tem;
// uint j;
// for(j=last_i+1; j<=last_i_nex; j++)
// {
// uint i_nex_tem2 = i_nex_tem;
// Eigen::Vector3d curr_direct2;
// uint ttem = plane_judge(pl, types, j, i_nex_tem, curr_direct2);
// if(ttem != 1)
// {
// i_nex_tem = i_nex_tem2;
// break;
// }
// curr_direct = curr_direct2;
// }
// if(j == last_i+1)
// {
// last_state = 0;
// }
// else
// {
// for(uint k=last_i_nex; k<=i_nex_tem; k++)
// {
// if(k != i_nex_tem)
// {
// types[k].ftype = Real_Plane;
// }
// else
// {
// types[k].ftype = Poss_Plane;
// }
// }
// i = i_nex_tem-1;
// i_nex = i_nex_tem;
// i2 = j-1;
// last_state = 1;
// }
// }
// }
last_i = i2;
last_i_nex = i_nex;
last_direct = curr_direct;
}
plsize2 = plsize > 3 ? plsize - 3 : 0;
for(uint i=head+3; i<plsize2; i++)
{
if(types[i].range<blind || types[i].ftype>=Real_Plane)
{
continue;
}
if(types[i-1].dista<1e-16 || types[i].dista<1e-16)
{
continue;
}
Eigen::Vector3d vec_a(pl[i].x, pl[i].y, pl[i].z);
Eigen::Vector3d vecs[2];
for(int j=0; j<2; j++)
{
int m = -1;
if(j == 1)
{
m = 1;
}
if(types[i+m].range < blind)
{
if(types[i].range > inf_bound)
{
types[i].edj[j] = Nr_inf;
}
else
{
types[i].edj[j] = Nr_blind;
}
continue;
}
vecs[j] = Eigen::Vector3d(pl[i+m].x, pl[i+m].y, pl[i+m].z);
vecs[j] = vecs[j] - vec_a;
types[i].angle[j] = vec_a.dot(vecs[j]) / vec_a.norm() / vecs[j].norm();
if(types[i].angle[j] < jump_up_limit)
{
types[i].edj[j] = Nr_180;
}
else if(types[i].angle[j] > jump_down_limit)
{
types[i].edj[j] = Nr_zero;
}
}
types[i].intersect = vecs[Prev].dot(vecs[Next]) / vecs[Prev].norm() / vecs[Next].norm();
if(types[i].edj[Prev]==Nr_nor && types[i].edj[Next]==Nr_zero && types[i].dista>0.0225 && types[i].dista>4*types[i-1].dista)
{
if(types[i].intersect > cos160)
{
if(edge_jump_judge(pl, types, i, Prev))
{
types[i].ftype = Edge_Jump;
}
}
}
else if(types[i].edj[Prev]==Nr_zero && types[i].edj[Next]== Nr_nor && types[i-1].dista>0.0225 && types[i-1].dista>4*types[i].dista)
{
if(types[i].intersect > cos160)
{
if(edge_jump_judge(pl, types, i, Next))
{
types[i].ftype = Edge_Jump;
}
}
}
else if(types[i].edj[Prev]==Nr_nor && types[i].edj[Next]==Nr_inf)
{
if(edge_jump_judge(pl, types, i, Prev))
{
types[i].ftype = Edge_Jump;
}
}
else if(types[i].edj[Prev]==Nr_inf && types[i].edj[Next]==Nr_nor)
{
if(edge_jump_judge(pl, types, i, Next))
{
types[i].ftype = Edge_Jump;
}
}
else if(types[i].edj[Prev]>Nr_nor && types[i].edj[Next]>Nr_nor)
{
if(types[i].ftype == Nor)
{
types[i].ftype = Wire;
}
}
}
plsize2 = plsize-1;
double ratio;
for(uint i=head+1; i<plsize2; i++)
{
if(types[i].range<blind || types[i-1].range<blind || types[i+1].range<blind)
{
continue;
}
if(types[i-1].dista<1e-8 || types[i].dista<1e-8)
{
continue;
}
if(types[i].ftype == Nor)
{
if(types[i-1].dista > types[i].dista)
{
ratio = types[i-1].dista / types[i].dista;
}
else
{
ratio = types[i].dista / types[i-1].dista;
}
if(types[i].intersect<smallp_intersect && ratio < smallp_ratio)
{
if(types[i-1].ftype == Nor)
{
types[i-1].ftype = Real_Plane;
}
if(types[i+1].ftype == Nor)
{
types[i+1].ftype = Real_Plane;
}
types[i].ftype = Real_Plane;
}
}
}
int last_surface = -1;
for(uint j=head; j<plsize; j++)
{
if(types[j].ftype==Poss_Plane || types[j].ftype==Real_Plane)
{
if(last_surface == -1)
{
last_surface = j;
}
if(j == uint(last_surface+point_filter_num-1))
{
PointType ap;
ap.x = pl[j].x;
ap.y = pl[j].y;
ap.z = pl[j].z;
ap.intensity = pl[j].intensity;
ap.curvature = pl[j].curvature;
pl_surf.push_back(ap);
last_surface = -1;
}
}
else
{
if(types[j].ftype==Edge_Jump || types[j].ftype==Edge_Plane)
{
pl_corn.push_back(pl[j]);
}
if(last_surface != -1)
{
PointType ap;
for(uint k=last_surface; k<j; k++)
{
ap.x += pl[k].x;
ap.y += pl[k].y;
ap.z += pl[k].z;
ap.intensity += pl[k].intensity;
ap.curvature += pl[k].curvature;
}
ap.x /= (j-last_surface);
ap.y /= (j-last_surface);
ap.z /= (j-last_surface);
ap.intensity /= (j-last_surface);
ap.curvature /= (j-last_surface);
pl_surf.push_back(ap);
}
last_surface = -1;
}
}
}
void Preprocess::pub_func(PointCloudXYZI &pl, const ros::Time &ct)
{
pl.height = 1; pl.width = pl.size();
sensor_msgs::PointCloud2 output;
pcl::toROSMsg(pl, output);
output.header.frame_id = "livox";
output.header.stamp = ct;
}
int Preprocess::plane_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i_cur, uint &i_nex, Eigen::Vector3d &curr_direct)
{
double group_dis = disA*types[i_cur].range + disB;
group_dis = group_dis * group_dis;
// i_nex = i_cur;
double two_dis;
vector<double> disarr;
disarr.reserve(20);
for(i_nex=i_cur; i_nex<i_cur+group_size; i_nex++)
{
if(types[i_nex].range < blind)
{
curr_direct.setZero();
return 2;
}
disarr.push_back(types[i_nex].dista);
}
for(;;)
{
if((i_cur >= pl.size()) || (i_nex >= pl.size())) break;
if(types[i_nex].range < blind)
{
curr_direct.setZero();
return 2;
}
vx = pl[i_nex].x - pl[i_cur].x;
vy = pl[i_nex].y - pl[i_cur].y;
vz = pl[i_nex].z - pl[i_cur].z;
two_dis = vx*vx + vy*vy + vz*vz;
if(two_dis >= group_dis)
{
break;
}
disarr.push_back(types[i_nex].dista);
i_nex++;
}
double leng_wid = 0;
double v1[3], v2[3];
for(uint j=i_cur+1; j<i_nex; j++)
{
if((j >= pl.size()) || (i_cur >= pl.size())) break;
v1[0] = pl[j].x - pl[i_cur].x;
v1[1] = pl[j].y - pl[i_cur].y;
v1[2] = pl[j].z - pl[i_cur].z;
v2[0] = v1[1]*vz - vy*v1[2];
v2[1] = v1[2]*vx - v1[0]*vz;
v2[2] = v1[0]*vy - vx*v1[1];
double lw = v2[0]*v2[0] + v2[1]*v2[1] + v2[2]*v2[2];
if(lw > leng_wid)
{
leng_wid = lw;
}
}
if((two_dis*two_dis/leng_wid) < p2l_ratio)
{
curr_direct.setZero();
return 0;
}
uint disarrsize = disarr.size();
for(uint j=0; j<disarrsize-1; j++)
{
for(uint k=j+1; k<disarrsize; k++)
{
if(disarr[j] < disarr[k])
{
leng_wid = disarr[j];
disarr[j] = disarr[k];
disarr[k] = leng_wid;
}
}
}
if(disarr[disarr.size()-2] < 1e-16)
{
curr_direct.setZero();
return 0;
}
if(lidar_type==AVIA)
{
double dismax_mid = disarr[0]/disarr[disarrsize/2];
double dismid_min = disarr[disarrsize/2]/disarr[disarrsize-2];
if(dismax_mid>=limit_maxmid || dismid_min>=limit_midmin)
{
curr_direct.setZero();
return 0;
}
}
else
{
double dismax_min = disarr[0] / disarr[disarrsize-2];
if(dismax_min >= limit_maxmin)
{
curr_direct.setZero();
return 0;
}
}
curr_direct << vx, vy, vz;
curr_direct.normalize();
return 1;
}
bool Preprocess::edge_jump_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i, Surround nor_dir)
{
if(nor_dir == 0)
{
if(types[i-1].range<blind || types[i-2].range<blind)
{
return false;
}
}
else if(nor_dir == 1)
{
if(types[i+1].range<blind || types[i+2].range<blind)
{
return false;
}
}
double d1 = types[i+nor_dir-1].dista;
double d2 = types[i+3*nor_dir-2].dista;
double d;
if(d1<d2)
{
d = d1;
d1 = d2;
d2 = d;
}
d1 = sqrt(d1);
d2 = sqrt(d2);
if(d1>edgea*d2 || (d1-d2)>edgeb)
{
return false;
}
return true;
}

124
src/FAST_LIO/src/preprocess.h Normal file
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#include <ros/ros.h>
#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/PointCloud2.h>
#include <livox_ros_driver2/CustomMsg.h>
using namespace std;
#define IS_VALID(a) ((abs(a)>1e8) ? true : false)
typedef pcl::PointXYZINormal PointType;
typedef pcl::PointCloud<PointType> PointCloudXYZI;
enum LID_TYPE{AVIA = 1, VELO16, OUST64}; //{1, 2, 3}
enum TIME_UNIT{SEC = 0, MS = 1, US = 2, NS = 3};
enum Feature{Nor, Poss_Plane, Real_Plane, Edge_Jump, Edge_Plane, Wire, ZeroPoint};
enum Surround{Prev, Next};
enum E_jump{Nr_nor, Nr_zero, Nr_180, Nr_inf, Nr_blind};
struct orgtype
{
double range;
double dista;
double angle[2];
double intersect;
E_jump edj[2];
Feature ftype;
orgtype()
{
range = 0;
edj[Prev] = Nr_nor;
edj[Next] = Nr_nor;
ftype = Nor;
intersect = 2;
}
};
namespace velodyne_ros {
struct EIGEN_ALIGN16 Point {
PCL_ADD_POINT4D;
float intensity;
float time;
uint16_t ring;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace velodyne_ros
POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(float, intensity, intensity)
(float, time, time)
(uint16_t, ring, ring)
)
namespace ouster_ros {
struct EIGEN_ALIGN16 Point {
PCL_ADD_POINT4D;
float intensity;
uint32_t t;
uint16_t reflectivity;
uint8_t ring;
uint16_t ambient;
uint32_t range;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace ouster_ros
// clang-format off
POINT_CLOUD_REGISTER_POINT_STRUCT(ouster_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(float, intensity, intensity)
// use std::uint32_t to avoid conflicting with pcl::uint32_t
(std::uint32_t, t, t)
(std::uint16_t, reflectivity, reflectivity)
(std::uint8_t, ring, ring)
(std::uint16_t, ambient, ambient)
(std::uint32_t, range, range)
)
class Preprocess
{
public:
// EIGEN_MAKE_ALIGNED_OPERATOR_NEW
Preprocess();
~Preprocess();
void process(const livox_ros_driver2::CustomMsg::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out);
void process(const sensor_msgs::PointCloud2::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out);
void set(bool feat_en, int lid_type, double bld, int pfilt_num);
// sensor_msgs::PointCloud2::ConstPtr pointcloud;
PointCloudXYZI pl_full, pl_corn, pl_surf;
PointCloudXYZI pl_buff[128]; //maximum 128 line lidar
vector<orgtype> typess[128]; //maximum 128 line lidar
float time_unit_scale;
int lidar_type, point_filter_num, N_SCANS, SCAN_RATE, time_unit;
double blind;
bool feature_enabled, given_offset_time;
ros::Publisher pub_full, pub_surf, pub_corn;
private:
void avia_handler(const livox_ros_driver2::CustomMsg::ConstPtr &msg);
void oust64_handler(const sensor_msgs::PointCloud2::ConstPtr &msg);
void velodyne_handler(const sensor_msgs::PointCloud2::ConstPtr &msg);
void give_feature(PointCloudXYZI &pl, vector<orgtype> &types);
void pub_func(PointCloudXYZI &pl, const ros::Time &ct);
int plane_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i, uint &i_nex, Eigen::Vector3d &curr_direct);
bool small_plane(const PointCloudXYZI &pl, vector<orgtype> &types, uint i_cur, uint &i_nex, Eigen::Vector3d &curr_direct);
bool edge_jump_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i, Surround nor_dir);
int group_size;
double disA, disB, inf_bound;
double limit_maxmid, limit_midmin, limit_maxmin;
double p2l_ratio;
double jump_up_limit, jump_down_limit;
double cos160;
double edgea, edgeb;
double smallp_intersect, smallp_ratio;
double vx, vy, vz;
};

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src/Point-LIO/.gitmodules vendored Normal file
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[submodule "ikd-Tree"]
path = ikd-Tree
url = https://github.com/hku-mars/ikd-Tree.git
branch = fast_lio
[submodule "include/IKFoM"]
path = include/IKFoM
url = https://github.com/hku-mars/IKFoM.git
branch = toolkit

83
src/Point-LIO/CMakeLists.txt Executable file
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cmake_minimum_required(VERSION 2.8.3)
project(point_lio)
SET(CMAKE_BUILD_TYPE "Debug")
ADD_COMPILE_OPTIONS(-std=c++14 )
ADD_COMPILE_OPTIONS(-std=c++14 )
set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )
add_definitions(-DROOT_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/\")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fexceptions" )
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -pthread -std=c++0x -std=c++14 -fexceptions")
message("Current CPU archtecture: ${CMAKE_SYSTEM_PROCESSOR}")
if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" )
include(ProcessorCount)
ProcessorCount(N)
message("Processer number: ${N}")
if(N GREATER 5)
add_definitions(-DMP_EN)
add_definitions(-DMP_PROC_NUM=4)
message("core for MP: 3")
elseif(N GREATER 3)
math(EXPR PROC_NUM "${N} - 2")
add_definitions(-DMP_EN)
add_definitions(-DMP_PROC_NUM="${PROC_NUM}")
message("core for MP: ${PROC_NUM}")
else()
add_definitions(-DMP_PROC_NUM=1)
endif()
else()
add_definitions(-DMP_PROC_NUM=1)
endif()
find_package(OpenMP QUIET)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
find_package(PythonLibs REQUIRED)
find_path(MATPLOTLIB_CPP_INCLUDE_DIRS "matplotlibcpp.h")
find_package(catkin REQUIRED COMPONENTS
geometry_msgs
nav_msgs
sensor_msgs
roscpp
rospy
std_msgs
pcl_ros
tf
livox_ros_driver2
message_generation
eigen_conversions
)
find_package(Eigen3 REQUIRED)
find_package(PCL 1.8 REQUIRED)
message(Eigen: ${EIGEN3_INCLUDE_DIR})
include_directories(
${catkin_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIR}
${PCL_INCLUDE_DIRS}
${PYTHON_INCLUDE_DIRS}
include)
catkin_package(
CATKIN_DEPENDS geometry_msgs nav_msgs roscpp rospy std_msgs message_runtime
DEPENDS EIGEN3 PCL
INCLUDE_DIRS
)
add_executable(pointlio_mapping src/laserMapping.cpp include/ikd-Tree/ikd_Tree.cpp src/parameters.cpp src/preprocess.cpp src/Estimator.cpp)
target_link_libraries(pointlio_mapping ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES})
target_include_directories(pointlio_mapping PRIVATE ${PYTHON_INCLUDE_DIRS})

339
src/Point-LIO/LICENSE Normal file
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@ -0,0 +1,339 @@
GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The licenses for most software are designed to take away your
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License is intended to guarantee your freedom to share and change free
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General Public License applies to most of the Free Software
Foundation's software and to any other program whose authors commit to
using it. (Some other Free Software Foundation software is covered by
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When we speak of free software, we are referring to freedom, not
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Also add information on how to contact you by electronic and paper mail.
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Ty Coon, President of Vice
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consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.

1
src/Point-LIO/Log/guide.md Executable file
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Here saved the debug records which can be drew by the ../log/plot.py. The record function can be found frm the MACRO: DEBUG_FILE_DIR(name) in common_lib.h.

0
src/Point-LIO/Log/imu.txt Executable file
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0
src/Point-LIO/Log/imu_pbp.txt Normal file
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0
src/Point-LIO/Log/mat_out.txt Normal file
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46
src/Point-LIO/Log/plot.py Executable file
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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
a_out=np.loadtxt('mat_out.txt')
#######for normal#######
fig, axs = plt.subplots(3,2)
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
time=a_out[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Velocity')
axs[0,1].set_title('bg')
axs[1,1].set_title('ba')
axs[2,1].set_title('Gravity')
for i in range(1,4):
for j in range(6):
axs[j%3, j//3].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(6):
axs[j%3, j//3].grid()
axs[j%3, j//3].legend()
plt.grid()
#######for normal#######
#### Draw IMU data
#fig, axs = plt.subplots(2)
#imu=np.loadtxt('imu_pbp.txt')
#time=imu[:,0]
#axs[0].set_title('Gyroscope')
#axs[1].set_title('Accelerameter')
#lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
#lab_2 = ['acc-x', 'acc-y', 'acc-z']
#for i in range(3):
#if i==1:
# axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
# axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
#for i in range(2):
#axs[i].set_xlim(386,389)
# axs[i].grid()
# axs[i].legend()
#plt.grid()
plt.show()

125
src/Point-LIO/Log/plot_imu.py Executable file
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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
#### Draw IMU data
fig, axs = plt.subplots(2)
imu=np.loadtxt('imu_pbp.txt')
time=imu[:,0]
axs[0].set_title('Gyroscope')
axs[1].set_title('Accelerameter')
lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
lab_2 = ['acc-x', 'acc-y', 'acc-z']
for i in range(3):
#if i==1:
axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
for i in range(2):
#axs[i].set_xlim(386,389)
axs[i].grid()
axs[i].legend()
plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(0,1977)
#len_time2 = np.arange(1977, 3954)
#len_time3 = np.arange(3954,5931)
#len_time4 = np.arange(5931,7908)
#len_time5 = np.arange(7908,9885)
#if i==1:
#axs[0].plot(len_time1, time[0:1977],'.-', label='check')
#axs[1].plot(len_time2, time[1977:3954],'.-', label='check')
#axs[2].plot(len_time3, time[3954:5931],'.-', label='check')
#axs[3].plot(len_time4, time[5931:7908],'.-', label='check')
#axs[4].plot(len_time5, time[7908:9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(9885,9885+1977)
#len_time2 = np.arange(9885+1977,9885+3954)
#len_time3 = np.arange(9885+3954,9885+5931)
#len_time4 = np.arange(9885+5931,9885+7908)
#len_time5 = np.arange(9885+7908,9885+9885)
#if i==1:
#axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check')
#axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check')
#axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check')
#axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check')
#axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
# #### Draw time calculation
# plt.figure(3)
# fig = plt.figure()
# font1 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 12,
# }
# c="red"
# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt')
# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt')
# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt')
# # n = a_out[:,1].size
# # time_mean = a_out[:,1].mean()
# # time_se = a_out[:,1].std() / np.sqrt(n)
# # time_err = a_out[:,1] - time_mean
# # feat_mean = a_out[:,2].mean()
# # feat_err = a_out[:,2] - feat_mean
# # feat_se = a_out[:,2].std() / np.sqrt(n)
# ax1 = fig.add_subplot(111)
# ax1.set_ylabel('Effective Feature Numbers',font1)
# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9])
# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9])
# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9])
# ax1.set_ylim([0, 3000])
# ax2 = ax1.twinx()
# ax2.spines['right'].set_color('red')
# ax2.set_ylabel('Compute Time (ms)',font1)
# ax2.yaxis.label.set_color('red')
# ax2.tick_params(axis='y', colors='red')
# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.set_xlim([0.5, 3.5])
# ax2.set_ylim([0, 100])
# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2'))
# # # print(time_se)
# # # print(a_out3[:,2])
# plt.grid()
# plt.savefig("time.pdf", dpi=1200)
plt.show()

178
src/Point-LIO/Log/plot_out.py Executable file
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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
# a_pre=np.loadtxt('mat_pre.txt')
a_out=np.loadtxt('mat_out.txt')
if((a_out.shape[1] != 19) & (a_out.shape[1] != 20)):
######for ikfom
fig, axs = plt.subplots(4,2)
#lab_pre = ['', 'pre-x', 'pre-y', 'pre-z']
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
time=a_out[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Velocity')
axs[3,0].set_title('Angular velocity')
axs[0,1].set_title('Acceleration')
axs[1,1].set_title('Gravity')
axs[2,1].set_title('bg')
axs[3,1].set_title('ba')
for i in range(1,4):
for j in range(8):
#axs[j%4, j//4].plot(time, a_pre[:,i+j*3],'.-', label=lab_pre[i])
axs[j%4, j//4].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(8):
# axs[j].set_xlim(386,389)
axs[j%4, j//4].grid()
axs[j%4, j//4].legend()
plt.grid()
######for ikfom#######
else:
#######for normal#######
fig, axs = plt.subplots(3,2)
lab_pre = ['', 'pre-x', 'pre-y', 'pre-z']
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
time=a_out[:,0]
time1 = a_pre[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Velocity')
axs[0,1].set_title('bg')
axs[1,1].set_title('ba')
axs[2,1].set_title('Gravity')
for i in range(1,4):
for j in range(6):
axs[j%3, j/3].plot(time1, a_pre[:,i+j*3],'.-', label=lab_pre[i])
axs[j%3, j/3].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(6):
# axs[j].set_xlim(386,389)
axs[j%3, j//3].grid()
axs[j%3, j//3].legend()
plt.grid()
#######for normal#######
#### Draw IMU data
fig, axs = plt.subplots(2)
imu=np.loadtxt('imu_pbp.txt')
time=imu[:,0]
axs[0].set_title('Gyroscope')
axs[1].set_title('Accelerameter')
lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
lab_2 = ['acc-x', 'acc-y', 'acc-z']
for i in range(3):
#if i==1:
axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
for i in range(2):
#axs[i].set_xlim(386,389)
axs[i].grid()
axs[i].legend()
plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(0,1977)
#len_time2 = np.arange(1977, 3954)
#len_time3 = np.arange(3954,5931)
#len_time4 = np.arange(5931,7908)
#len_time5 = np.arange(7908,9885)
#if i==1:
#axs[0].plot(len_time1, time[0:1977],'.-', label='check')
#axs[1].plot(len_time2, time[1977:3954],'.-', label='check')
#axs[2].plot(len_time3, time[3954:5931],'.-', label='check')
#axs[3].plot(len_time4, time[5931:7908],'.-', label='check')
#axs[4].plot(len_time5, time[7908:9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(9885,9885+1977)
#len_time2 = np.arange(9885+1977,9885+3954)
#len_time3 = np.arange(9885+3954,9885+5931)
#len_time4 = np.arange(9885+5931,9885+7908)
#len_time5 = np.arange(9885+7908,9885+9885)
#if i==1:
#axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check')
#axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check')
#axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check')
#axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check')
#axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
# #### Draw time calculation
# plt.figure(3)
# fig = plt.figure()
# font1 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 12,
# }
# c="red"
# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt')
# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt')
# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt')
# # n = a_out[:,1].size
# # time_mean = a_out[:,1].mean()
# # time_se = a_out[:,1].std() / np.sqrt(n)
# # time_err = a_out[:,1] - time_mean
# # feat_mean = a_out[:,2].mean()
# # feat_err = a_out[:,2] - feat_mean
# # feat_se = a_out[:,2].std() / np.sqrt(n)
# ax1 = fig.add_subplot(111)
# ax1.set_ylabel('Effective Feature Numbers',font1)
# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9])
# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9])
# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9])
# ax1.set_ylim([0, 3000])
# ax2 = ax1.twinx()
# ax2.spines['right'].set_color('red')
# ax2.set_ylabel('Compute Time (ms)',font1)
# ax2.yaxis.label.set_color('red')
# ax2.tick_params(axis='y', colors='red')
# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.set_xlim([0.5, 3.5])
# ax2.set_ylim([0, 100])
# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2'))
# # # print(time_se)
# # # print(a_out3[:,2])
# plt.grid()
# plt.savefig("time.pdf", dpi=1200)
plt.show()

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# Point-LIO
## 1. Introduction
<div align="center">
<div align="center">
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/toc4.png" width = 75% >
</div>
<font color=#a0a0a0 size=2>The framework and key points of the Point-LIO.</font>
</div>
**New features:**
1. would not fly under degeneration.
2. high odometry output frequency, 4k-8kHz.
3. robust to IMU saturation and severe vibration, and other aggressive motions (75 rad/s in our test).
4. no motion distortion.
5. computationally efficient, robust, versatile on public datasets with general motions.
6. As an odometry, Point-LIO could be used in various autonomous tasks, such as trajectory planning, control, and perception, especially in cases involving very fast ego-motions (e.g., in the presence of severe vibration and high angular or linear velocity) or requiring high-rate odometry output and mapping (e.g., for high-rate feedback control and perception).
**Important notes:**
A. Please make sure the IMU and LiDAR are **Synchronized**, that's important.
B. Please obtain the saturation values of your used IMU (i.e., accelerator and gyroscope), and the units of the accelerator of your used IMU, then modify the .yaml file according to those settings, including values of 'satu_acc', 'satu_gyro', 'acc_norm'. That's improtant.
C. The warning message "Failed to find match for field 'time'." means the timestamps of each LiDAR points are missed in the rosbag file. That is important because Point-LIO processes at the sampling time of each LiDAR point.
D. We recommend to set the **extrinsic_est_en** to false if the extrinsic is given. As for the extrinsic initiallization, please refer to our recent work: [**Robust and Online LiDAR-inertial Initialization**](https://github.com/hku-mars/LiDAR_IMU_Init).
E. If a high odometry output frequency without downsample is required, set ``` publish_odometry_without_downsample ``` as true. Then the warning message of tf "TF_REPEATED_DATA" will pop up in the terminal window, because the time interval between two publish odometery is too small. The following command could be used to suppress this warning to a smaller frequency:
in your catkin_ws/src,
git clone --branch throttle-tf-repeated-data-error git@github.com:BadgerTechnologies/geometry2.git
Then rebuild, source setup.bash, run and then it should be reduced down to once every 10 seconds. If 10 seconds is still too much log output then change the ros::Duration(10.0) to 10000 seconds or whatever you like.
F. If you want to use Point-LIO without imu, set the "imu_en" as false, and provide a predefined value of gavity in "gravity_init" as true as possible in the yaml file, and keep the "use_imu_as_input" as 0.
## **1.1. Developers:**
The codes of this repo are contributed by:
[Dongjiao He (贺东娇)](https://github.com/Joanna-HE) and [Wei Xu (徐威)](https://github.com/XW-HKU)
## **1.2. Related paper**
Our paper is published on Advanced Intelligent Systems(AIS). [Point-LIO](https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202200459), DOI: 10.1002/aisy.202200459
## **1.3. Related video**
Our accompany video is available on **YouTube**.
<div align="center">
<a href="https://youtu.be/oS83xUs42Uw" target="_blank"><img src="https://github.com/hku-mars/Point-LIO/raw/master/image/final.png" width=60% /></a>
</div>
## 2. What can Point-LIO do?
### 2.1 Simultaneous LiDAR localization and mapping (SLAM) without motion distortion
### 2.2 Produce high odometry output frequence and high bandwidth
### 2.3 SLAM with aggressive motions even the IMU is saturated
# **3. Prerequisites**
## **3.1 Ubuntu and [ROS](https://www.ros.org/)**
We tested our code on Ubuntu20.04 with noetic. Ubuntu18.04 and lower versions have problems of environments to support the Point-LIO, try to avoid using Point-LIO in those systems. Additional ROS package is required:
```
sudo apt-get install ros-xxx-pcl-conversions
```
## **3.2 Eigen**
Following the official [Eigen installation](eigen.tuxfamily.org/index.php?title=Main_Page), or directly install Eigen by:
```
sudo apt-get install libeigen3-dev
```
## **3.3 livox_ros_driver**
Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
*Remarks:*
- Since the Point-LIO supports Livox serials LiDAR, so the **livox_ros_driver** must be installed and **sourced** before run any Point-LIO luanch file.
- How to source? The easiest way is add the line ``` source $Licox_ros_driver_dir$/devel/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Licox_ros_driver_dir$ ``` is the directory of the livox ros driver workspace (should be the ``` ws_livox ``` directory if you completely followed the livox official document).
## 4. Build
Clone the repository and catkin_make:
```
cd ~/$A_ROS_DIR$/src
git clone https://github.com/hku-mars/Point-LIO.git
cd Point-LIO
git submodule update --init
cd ../..
catkin_make
source devel/setup.bash
```
- Remember to source the livox_ros_driver before build (follow 3.3 **livox_ros_driver**)
- If you want to use a custom build of PCL, add the following line to ~/.bashrc
```export PCL_ROOT={CUSTOM_PCL_PATH}```
## 5. Directly run
### 5.1 For Avia
Connect to your PC to Livox Avia LiDAR by following [Livox-ros-driver installation](https://github.com/Livox-SDK/livox_ros_driver), then
```
cd ~/$Point_LIO_ROS_DIR$
source devel/setup.bash
roslaunch point_lio mapping_avia.launch
roslaunch livox_ros_driver livox_lidar_msg.launch
```
- For livox serials, Point-LIO only support the data collected by the ``` livox_lidar_msg.launch ``` since only its ``` livox_ros_driver/CustomMsg ``` data structure produces the timestamp of each LiDAR point which is very important for Point-LIO. ``` livox_lidar.launch ``` can not produce it right now.
- If you want to change the frame rate, please modify the **publish_freq** parameter in the [livox_lidar_msg.launch](https://github.com/Livox-SDK/livox_ros_driver/blob/master/livox_ros_driver/launch/livox_lidar_msg.launch) of [Livox-ros-driver](https://github.com/Livox-SDK/livox_ros_driver) before make the livox_ros_driver pakage.
### 5.2 For Livox serials with external IMU
mapping_avia.launch theratically supports mid-70, mid-40 or other livox serial LiDAR, but need to setup some parameters befor run:
Edit ``` config/avia.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ```
3. Translational extrinsic: ``` extrinsic_T ```
4. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame). They can be found in the official manual.
5. Saturation value of IMU's accelerator and gyroscope: ```satu_acc```, ```satu_gyro```
6. The norm of IMU's acceleration according to unit of acceleration messages: ``` acc_norm ```
### 5.3 For Velodyne or Ouster (Velodyne as an example)
Step A: Setup before run
Edit ``` config/velodyne.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ``` (both internal and external, 6-aixes or 9-axies are fine)
3. Set the parameter ```timestamp_unit``` based on the unit of **time** (Velodyne) or **t** (Ouster) field in PoindCloud2 rostopic
4. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): ``` scan_line ```
5. Translational extrinsic: ``` extrinsic_T ```
6. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).
7. Saturation value of IMU's accelerator and gyroscope: ```satu_acc```, ```satu_gyro```
8. The norm of IMU's acceleration according to unit of acceleration messages: ``` acc_norm ```
Step B: Run below
```
cd ~/$Point_LIO_ROS_DIR$
source devel/setup.bash
roslaunch point_lio mapping_velody16.launch
```
Step C: Run LiDAR's ros driver or play rosbag.
### 5.4 PCD file save
Set ``` pcd_save_enable ``` in launchfile to ``` 1 ```. All the scans (in global frame) will be accumulated and saved to the file ``` Point-LIO/PCD/scans.pcd ``` after the Point-LIO is terminated. ```pcl_viewer scans.pcd``` can visualize the point clouds.
*Tips for pcl_viewer:*
- change what to visualize/color by pressing keyboard 1,2,3,4,5 when pcl_viewer is running.
```
1 is all random
2 is X values
3 is Y values
4 is Z values
5 is intensity
```
# **6. Examples**
The example datasets could be downloaded through [onedrive](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/hdj65822_connect_hku_hk/EmRJYy4ZfAlMiIJ786ogCPoBcGQ2BAchuXjE5oJQjrQu0Q?e=igu44W). Pay attention that if you want to test on racing_drone.bag, [0.0, 9.810, 0.0] should be input in 'mapping/gravity_init' in avia.yaml, and set the 'start_in_aggressive_motion' as true in the yaml. Because this bag start from a high speed motion. And for PULSAR.bag, we change the measuring range of the gyroscope of the built-in IMU to 17.5 rad/s. Therefore, when you test on this bag, please change 'satu_gyro' to 17.5 in avia.yaml.
## **6.1. Example-1: SLAM on datasets with aggressive motions where IMU is saturated**
<div align="center">
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example1.gif" width="40%" />
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example2.gif" width="54%" />
</div>
## **6.2. Example-2: Application on FPV and PULSAR**
<div align="center">
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example3.gif" width="58%" />
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example4.gif" width="35%" />
</div>
PULSAR is a self-rotating UAV actuated by only one motor, [PULSAR](https://github.com/hku-mars/PULSAR)
## 7. Contact us
If you have any questions about this work, please feel free to contact me <hdj65822ATconnect.hku.hk> and Dr. Fu Zhang <fuzhangAThku.hk> via email.

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 1
scan_line: 6
timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 1.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.001 # 0.001; 0.01
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 0.1
imu_meas_omg_cov: 0.1 #0.01 # 0.1
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
fov_degree: 360
det_range: 450.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0.04165, 0.02326, -0.0284 ]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme),
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 1
scan_line: 6
timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 4.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.01 #0.1 # 2
imu_meas_omg_cov: 0.01 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
fov_degree: 100
det_range: 260.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0.05512, 0.02226, -0.0297 ]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

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common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 1
scan_line: 4
timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 0.5
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.001 # 0.001; 0.01
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 0.1
imu_meas_omg_cov: 0.1 #0.01 # 0.1
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
fov_degree: 360
det_range: 100
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ -0.011, -0.02329, 0.04412 ]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

57
src/Point-LIO/config/ouster64.yaml Executable file
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common:
lid_topic: "/os_cloud_node/points"
imu_topic: "/os_cloud_node/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 3 # 2 #velodyne # 1 Livox Avia LiDAR
scan_line: 32 # 32 #velodyne 6 avia
timestamp_unit: 3 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 0.20
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.01 # = 1 / frequency of IMU
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.1 # 0.01
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
fov_degree: 180
det_range: 150.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [0.0, 0.0, 0.0]
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

63
src/Point-LIO/config/velody16.yaml Executable file
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common:
lid_topic: "/velodyne_points"
imu_topic: "/imu/data"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 2
scan_line: 32
timestamp_unit: 2 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 2.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.01 # = 1 / frequency of IMU
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500
gyr_cov_output: 1000
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81
fov_degree: 180
det_range: 100.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0, 0, 0.28] # ulhk # [-0.5, 1.4, 1.5] # utbm
# extrinsic_R: [ 0, 1, 0,
# -1, 0, 0,
# 0, 0, 1 ] # ulhk 5 6
# extrinsic_R: [ 0, -1, 0,
# 1, 0, 0,
# 0, 0, 1 ] # utbm 1, 2
extrinsic_R: [ 1, 0, 0,
0, 1, 0,
0, 0, 1 ] # ulhk 4 utbm 3
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

18
src/Point-LIO/include/.vscode/c_cpp_properties.json vendored Executable file
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{
"configurations": [
{
"browse": {
"databaseFilename": "",
"limitSymbolsToIncludedHeaders": true
},
"includePath": [
"/home/ecstasy/catkin_ws/devel/include/**",
"/opt/ros/melodic/include/**",
"/home/ecstasy/catkin_ws/src/FAST_LIO/include/**",
"/usr/include/**"
],
"name": "ROS"
}
],
"version": 4
}

6
src/Point-LIO/include/.vscode/settings.json vendored Executable file
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@ -0,0 +1,6 @@
{
"python.autoComplete.extraPaths": [
"/home/ecstasy/catkin_ws/devel/lib/python2.7/dist-packages",
"/opt/ros/melodic/lib/python2.7/dist-packages"
]
}

472
src/Point-LIO/include/FOV_Checker/FOV_Checker.cpp Executable file
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#include "FOV_Checker.h"
FOV_Checker::FOV_Checker(){
// fp = fopen("/home/ecstasy/catkin_ws/fov_data.csv","w");
// fprintf(fp,"cur_pose_x,cur_pose_y,cur_pose_z,axis_x,axis_y,axis_z,theta,depth\n");
// fclose(fp);
}
FOV_Checker::~FOV_Checker(){
}
void FOV_Checker::Set_Env(BoxPointType env_param){
env = env_param;
}
void FOV_Checker::Set_BoxLength(double box_len_param){
box_length = box_len_param;
}
void round_v3d(Eigen::Vector3d &vec, int decimal){
double tmp;
int t;
for (int i = 0; i < 3; i++){
t = pow(10,decimal);
tmp = round(vec(i)*t);
vec(i) = tmp/t;
}
return;
}
void FOV_Checker::check_fov(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, vector<BoxPointType> &boxes){
round_v3d(cur_pose,4);
round_v3d(axis,3);
axis = axis/axis.norm();
// fp = fopen("/home/ecstasy/catkin_ws/fov_data.csv","a");
// fprintf(fp,"%f,%f,%f,%f,%f,%f,%0.4f,%0.1f,",cur_pose(0),cur_pose(1),cur_pose(2),axis(0),axis(1),axis(2),theta,depth);
// fclose(fp);
// cout << "cur_pose: " << cur_pose.transpose() << endl;
// cout<< "axis: " << axis.transpose() << endl;
// cout<< "theta: " << theta << " depth: " << depth << endl;
// cout<< "env: " << env.vertex_min[0] << " " << env.vertex_max[0] << endl;
double axis_angle[6], min_angle, gap, plane_u_min, plane_u_max;
Eigen::Vector3d plane_w, plane_u, plane_v, center_point, start_point, box_p;
Eigen::Vector3d box_p_min, box_p_max;
int i, j, k, index, maxn, start_i, max_uN, max_vN, max_ulogN, u_min, u_max;
bool flag = false, box_found = false;
boxes.clear();
BoxPointType box;
axis_angle[0] = acos(axis(0));
axis_angle[1] = acos(axis(1));
axis_angle[2] = acos(axis(2));
axis_angle[3] = acos(-axis(0));
axis_angle[4] = acos(-axis(1));
axis_angle[5] = acos(-axis(2));
index = 1;
min_angle = axis_angle[0];
for (i=1;i<6;i++){
if (axis_angle[i]<min_angle){
min_angle = axis_angle[i];
index = i+1;
}
}
switch (index){
case 1:
// YZ plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = floor(cur_pose(0)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(0);
maxn = ceil((env.vertex_max[0]-cur_pose(0))/box_length) +1;
start_i = 0;
break;
case 2:
// XZ plane
plane_w = Eigen::Vector3d(0,1,0);
plane_u = Eigen::Vector3d(1,0,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = floor(cur_pose(1)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(1);
maxn = ceil((env.vertex_max[1]-cur_pose(1))/box_length) +1;
start_i = 0;
break;
case 3:
// XY plane
plane_w = Eigen::Vector3d(0,0,1);
plane_u = Eigen::Vector3d(1,0,0);
plane_v = Eigen::Vector3d(0,1,0);
gap = floor(cur_pose(2)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(2);
maxn = ceil((env.vertex_max[2]-cur_pose(2))/box_length) +1;
start_i = 0;
break;
case 4:
// YZ plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(0)/box_length - 0.5 - eps_value) * box_length - 0.5 * box_length - cur_pose(0);
maxn = ceil((cur_pose(0)-env.vertex_min[0])/box_length) +1;
start_i = 1;
break;
case 5:
// XZ plane
plane_w = Eigen::Vector3d(0,1,0);
plane_u = Eigen::Vector3d(1,0,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(1)/box_length - 0.5 - eps_value) * box_length - 0.5 * box_length - cur_pose(1);
maxn = ceil((cur_pose(1)-env.vertex_min[1])/box_length) +1;
start_i = 1;
break;
case 6:
// XY plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(2)/box_length - 0.5 - eps_value) * box_length - 0.5 * box_length - cur_pose(2);
maxn = ceil((cur_pose(2)-env.vertex_min[2])/box_length) +1;
start_i = 1;
break;
default:
// YZ plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(0)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(0);
maxn = ceil((env.vertex_max[0]-cur_pose(0))/box_length) +1;
start_i = 0;
break;
}
for (i=start_i; i<=maxn; i++){
center_point = cur_pose + (abs(gap) + (i-1) * box_length)/cos(min_angle) * axis;
if (index == 1 || index == 4){
start_point = Eigen::Vector3d(center_point(0),floor(center_point(1)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length,floor(center_point(2)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length);
max_uN = ceil((env.vertex_max[1]-env.vertex_min[1])/box_length);
max_ulogN = floor(log2(max_uN));
max_vN = ceil((env.vertex_max[2]-env.vertex_min[2])/box_length);
plane_u_min = env.vertex_min[1];
plane_u_max = env.vertex_max[1];
} else {
if (index == 2 || index == 5){
start_point = Eigen::Vector3d(floor(center_point(0)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length, center_point(1), floor(center_point(2)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length);
max_uN = ceil((env.vertex_max[0]-env.vertex_min[0])/box_length);
max_ulogN = floor(log2(max_uN));
max_vN = ceil((env.vertex_max[2]-env.vertex_min[2])/box_length);
plane_u_min = env.vertex_min[0];
plane_u_max = env.vertex_max[0];
} else {
start_point = Eigen::Vector3d(floor(center_point(0)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length, floor(center_point(1)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length, center_point(2));
max_uN = ceil((env.vertex_max[1]-env.vertex_min[1])/box_length);
max_ulogN = floor(log2(max_uN));
max_vN = ceil((env.vertex_max[2]-env.vertex_min[2])/box_length);
plane_u_min = env.vertex_min[1];
plane_u_max = env.vertex_max[1];
}
}
flag = false;
for (j = 1; j <= max_vN; j++){
k = max_ulogN;
u_min = 0;
box_p_min = start_point.cwiseProduct(plane_w + plane_v) + plane_u * plane_u_min + plane_v * box_length * (j-1);
box_p_max = plane_u * plane_u_max + start_point.cwiseProduct(plane_w + plane_v) + plane_v * box_length * j + plane_w * box_length;
//printf("---- UPSIDE (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box_p_min[0],box_p_min[1],box_p_min[2],box_p_max[0],box_p_max[1],box_p_max[2]);
while (k>=0){
box_p = box_p_min + plane_u * box_length * (u_min + pow(2,k)) + plane_v * box_length + plane_w * box_length;
box.vertex_min[0] = box_p_min(0);
box.vertex_min[1] = box_p_min(1);
box.vertex_min[2] = box_p_min(2);
box.vertex_max[0] = box_p(0);
box.vertex_max[1] = box_p(1);
box.vertex_max[2] = box_p(2);
if (!check_box(cur_pose, axis, theta, depth, box)) u_min = u_min + pow(2,k);
k = k-1;
}
k = max_ulogN;
u_max = 0;
while (k>=0){
box_p = box_p_max - plane_u * box_length * (u_max + pow(2,k)) - plane_v * box_length - plane_w * box_length;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p_max(0);
box.vertex_max[1] = box_p_max(1);
box.vertex_max[2] = box_p_max(2);
if (!check_box(cur_pose, axis, theta, depth, box)) u_max = u_max + pow(2,k);
k = k-1;
}
u_max = max(0, max_uN - u_max - 1);
box_found = false;
//printf("---- u_min -> u_max: %d->%d\n",u_min,u_max);
for (k = u_min; k <= u_max; k++){
box_p = box_p_min + plane_u * box_length * k;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p(0) + box_length;
box.vertex_max[1] = box_p(1) + box_length;
box.vertex_max[2] = box_p(2) + box_length;
if (check_box_in_env(box)){
//printf("---- FOUND: (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box.vertex_min[0],box.vertex_min[1],box.vertex_min[2],box.vertex_max[0],box.vertex_max[1],box.vertex_max[2]);
box_found = true;
boxes.push_back(box);
}
}
if (box_found) {
flag = true;
} else {
if (j>1) break;
}
}
for (j = 1; j <= max_vN; j++){
k = max_ulogN;
u_min = 0;
box_p_min = start_point.cwiseProduct(plane_w + plane_v) + plane_u * plane_u_min - plane_v * box_length * j;
box_p_max = plane_u * plane_u_max + start_point.cwiseProduct(plane_w + plane_v) - plane_v * box_length * (j-1) + plane_w * box_length;
//printf("---- DOWNSIDE (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box_p_min[0],box_p_min[1],box_p_min[2],box_p_max[0],box_p_max[1],box_p_max[2]);
while (k>=0){
box_p = box_p_min + plane_u * box_length * (u_min + pow(2,k)) + plane_v * box_length + plane_w * box_length;
box.vertex_min[0] = box_p_min(0);
box.vertex_min[1] = box_p_min(1);
box.vertex_min[2] = box_p_min(2);
box.vertex_max[0] = box_p(0);
box.vertex_max[1] = box_p(1);
box.vertex_max[2] = box_p(2);
if (!check_box(cur_pose, axis, theta, depth, box)) u_min = u_min + pow(2,k);
k = k-1;
}
k = max_ulogN;
u_max = 0;
while (k>=0){
box_p = box_p_max - plane_u * box_length * (u_max + pow(2,k)) - plane_v * box_length - plane_w * box_length;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p_max(0);
box.vertex_max[1] = box_p_max(1);
box.vertex_max[2] = box_p_max(2);
if (!check_box(cur_pose, axis, theta, depth, box)) {
u_max = u_max + pow(2,k);
// printf("-------- Not Included: (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box.vertex_min[0],box.vertex_min[1],box.vertex_min[2],box.vertex_max[0],box.vertex_max[1],box.vertex_max[2]);
}
k = k-1;
}
u_max = max(0, max_uN - u_max - 1);
//printf("---- u_min -> u_max: %d->%d\n",u_min,u_max);
box_found = 0;
for (k = u_min; k <= u_max; k++){
box_p = box_p_min + plane_u * box_length * k;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p(0) + box_length;
box.vertex_max[1] = box_p(1) + box_length;
box.vertex_max[2] = box_p(2) + box_length;
if (check_box_in_env(box)){
//printf("---- FOUND: (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box.vertex_min[0],box.vertex_min[1],box.vertex_min[2],box.vertex_max[0],box.vertex_max[1],box.vertex_max[2]);
box_found = 1;
boxes.push_back(box);
}
}
if (box_found) {
flag = true;
} else {
if (j>1) break;
}
}
if (!flag && i>0) break;
}
}
bool FOV_Checker::check_box(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, const BoxPointType box){
Eigen::Vector3d vertex[8];
bool s;
vertex[0] = Eigen::Vector3d(box.vertex_min[0], box.vertex_min[1], box.vertex_min[2]);
vertex[1] = Eigen::Vector3d(box.vertex_min[0], box.vertex_min[1], box.vertex_max[2]);
vertex[2] = Eigen::Vector3d(box.vertex_min[0], box.vertex_max[1], box.vertex_min[2]);
vertex[3] = Eigen::Vector3d(box.vertex_min[0], box.vertex_max[1], box.vertex_max[2]);
vertex[4] = Eigen::Vector3d(box.vertex_max[0], box.vertex_min[1], box.vertex_min[2]);
vertex[5] = Eigen::Vector3d(box.vertex_max[0], box.vertex_min[1], box.vertex_max[2]);
vertex[6] = Eigen::Vector3d(box.vertex_max[0], box.vertex_max[1], box.vertex_min[2]);
vertex[7] = Eigen::Vector3d(box.vertex_max[0], box.vertex_max[1], box.vertex_max[2]);
for (int i = 0; i < 8; i++){
if (check_point(cur_pose, axis, theta, depth, vertex[i])){
return true;
}
}
Eigen::Vector3d center_point = (vertex[7]+vertex[0])/2.0;
if (check_point(cur_pose, axis, theta, depth, center_point)){
return true;
}
PlaneType plane[6];
plane[0].p[0] = vertex[0];
plane[0].p[1] = vertex[2];
plane[0].p[2] = vertex[1];
plane[0].p[3] = vertex[3];
plane[1].p[0] = vertex[0];
plane[1].p[1] = vertex[4];
plane[1].p[2] = vertex[2];
plane[1].p[3] = vertex[6];
plane[2].p[0] = vertex[0];
plane[2].p[1] = vertex[4];
plane[2].p[2] = vertex[1];
plane[2].p[3] = vertex[5];
plane[3].p[0] = vertex[4];
plane[3].p[1] = vertex[6];
plane[3].p[2] = vertex[5];
plane[3].p[3] = vertex[7];
plane[4].p[0] = vertex[2];
plane[4].p[1] = vertex[6];
plane[4].p[2] = vertex[3];
plane[4].p[3] = vertex[7];
plane[5].p[0] = vertex[1];
plane[5].p[1] = vertex[5];
plane[5].p[2] = vertex[3];
plane[5].p[3] = vertex[7];
if (check_surface(cur_pose, axis, theta, depth, plane[0]) || check_surface(cur_pose, axis, theta, depth, plane[1]) || check_surface(cur_pose, axis, theta, depth, plane[2]) || check_surface(cur_pose, axis, theta, depth, plane[3]) || check_surface(cur_pose, axis, theta, depth, plane[4]) || check_surface(cur_pose, axis, theta, depth, plane[5]))
s = 1;
else
s = 0;
return s;
}
bool FOV_Checker::check_surface(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, PlaneType plane){
Eigen::Vector3d plane_p, plane_u, plane_v, plane_w, pc, p, vec;
bool s;
double t, vec_dot_u, vec_dot_v;
plane_p = plane.p[0];
plane_u = plane.p[1] - plane_p;
plane_v = plane.p[2] - plane_p;
if (check_line(cur_pose, axis, theta, depth, plane_p, plane_u) || check_line(cur_pose, axis, theta, depth, plane_p, plane_v) || check_line(cur_pose, axis, theta, depth, plane_p + plane_u, plane_v) || check_line(cur_pose, axis, theta, depth, plane_p + plane_v, plane_u)){
s = 1;
return s;
}
pc = plane_p + (plane.p[3]-plane.p[0])/2;
if (check_point(cur_pose, axis, theta, depth, pc)){
s = 1;
return s;
}
plane_w = plane_u.cross(plane_v);
p = plane_p - cur_pose;
t = (p.dot(plane_w))/(axis.dot(plane_w));
vec = cur_pose + t * axis - plane_p;
vec_dot_u = vec.dot(plane_u)/plane_u.norm();
vec_dot_v = vec.dot(plane_v)/plane_v.norm();
if (t>=-eps_value && t<=depth && vec_dot_u>=-eps_value && vec_dot_u<=plane_u.norm() && vec_dot_v>=-eps_value && vec_dot_v <= plane_v.norm())
s = 1;
else
s = 0;
return s;
}
bool FOV_Checker::check_line(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d line_p, Eigen::Vector3d line_vec){
Eigen::Vector3d p, vec_1, vec_2;
double xl, yl, zl, xn, yn, zn, dot_1, dot_2, ln, pn, pl, l2, p2;
double A, B, C, delta, t1, t2;
bool s;
p = line_p - cur_pose;
xl = line_vec(0); yl = line_vec(1); zl = line_vec(2);
xn = axis(0); yn = axis(1); zn = axis(2);
vec_1 = line_p - cur_pose;
vec_2 = line_p + line_vec - cur_pose;
dot_1 = vec_1.dot(axis);
dot_2 = vec_2.dot(axis);
//printf("xl yl zl: %0.4f, %0.4f, %0.4f\n", xl, yl, zl);
//printf("xn yn zn: %0.4f, %0.4f, %0.4f\n", xn, yn, zn);
//printf("dot_1, dot_2, %0.4f, %0.4f\n",dot_1, dot_2);
if ((dot_1<0 && dot_2<0) || (dot_1>depth && dot_2>depth)){
s = false;
return s;
}
ln = xl*xn+yl*yn+zl*zn;
pn = p(0)*xn+p(1)*yn+p(2)*zn;
pl = p(0)*xl+p(1)*yl+p(2)*zl;
l2 = xl*xl+yl*yl+zl*zl;
p2 = p.norm()*p.norm();
//printf("ln: %0.4f\n",ln);
//printf("pn:%0.4f\n",pn);
//printf("pl:%0.4f\n",pl);
//printf("l2:%0.4f\n",l2);
//printf("p2:%0.4f\n",p2);
//printf("theta, cos(theta):%0.4f %0.4f\n",theta,cos(theta));
A = ln * ln - l2 * cos(theta) * cos(theta);
B = 2 * pn * ln - 2 * cos(theta) * cos(theta)*pl;
C = pn * pn - p2 * cos(theta) * cos(theta);
//printf("A:%0.4f, B:%0.4f, C:%0.4f\n", A,B,C);
if (!(fabs(A)<=eps_value)){
delta = B*B - 4*A*C;
//printf("delta: %0.4f\n",delta);
if (delta <= eps_value){
if (A < -eps_value){
s = false;
return s;
}
else{
s = true;
return s;
}
} else {
double sqrt_delta = sqrt(delta);
t1 = (-B - sqrt_delta)/(2*A);
t2 = (-B + sqrt_delta)/(2*A);
if (t1>t2) swap(t1,t2);
//printf("t1,t2: %0.4f,%0.4f\n",t1,t2);
// printf("%d\n",check_point(cur_pose, axis, theta, depth, line_p + line_vec * t1));
if ((t1>=-eps_value && t1<=1+eps_value) && check_point(cur_pose, axis, theta, depth, line_p + line_vec * t1)){
s = true;
return s;
}
// printf("%d\n",check_point(cur_pose, axis, theta, depth, line_p + line_vec * t2));
if ((t2>=-eps_value && t2<=1+eps_value) && check_point(cur_pose, axis, theta, depth, line_p + line_vec * t2)){
s = true;
return s;
}
if (A>-eps_value && (t2<eps_value || t1>1-eps_value)){
s = true;
return s;
}
if (A<eps_value && t1<eps_value && t2>1-eps_value){
s = true;
return s;
}
s = false;
}
} else{
if (!(fabs(B)<=eps_value)){
s = (B>-eps_value && -C/B<=1+eps_value) || (B<eps_value && -C/B>=-eps_value);
return s;
}
else {
s = C>=-eps_value;
return s;
}
}
return false;
}
bool FOV_Checker::check_point(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d point){
Eigen::Vector3d vec;
double proj_len;
bool s;
vec = point-cur_pose;
if (vec.transpose()*vec < 0.4 * box_length * box_length){
return true;
}
proj_len = vec.dot(axis);
if (proj_len > depth){
s = false;
return s;
}
//printf("acos: %0.4f\n",acos(proj_len/vec.norm()));
if (fabs(vec.norm()) <= 1e-4 || acos(proj_len/vec.norm()) <= theta + 0.0175)
s = true;
else
s = false;
return s;
}
bool FOV_Checker::check_box_in_env(BoxPointType box){
if (box.vertex_min[0] >= env.vertex_min[0]-eps_value && box.vertex_min[1] >= env.vertex_min[1]-eps_value && box.vertex_min[2] >= env.vertex_min[2]-eps_value && box.vertex_max[0]<= env.vertex_max[0]+eps_value && box.vertex_max[1]<= env.vertex_max[1]+eps_value && box.vertex_max[2]<= env.vertex_max[2]+eps_value){
return true;
} else {
return false;
}
}

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// Include Files
#pragma once
#include <math.h>
#include <cmath>
#include "ikd-Tree/ikd_Tree.h"
#include <Eigen/Core>
#include <algorithm>
#define eps_value 1e-6
struct PlaneType{
Eigen::Vector3d p[4];
};
class FOV_Checker{
public:
FOV_Checker();
~FOV_Checker();
void Set_Env(BoxPointType env_param);
void Set_BoxLength(double box_len_param);
void check_fov(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, vector<BoxPointType> &boxes);
bool check_box(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, const BoxPointType box);
bool check_line(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d line_p, Eigen::Vector3d line_vec);
bool check_surface(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, PlaneType plane);
bool check_point(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d point);
bool check_box_in_env(BoxPointType box);
private:
BoxPointType env;
double box_length;
FILE *fp;
};

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# Prerequisites
*.d
# Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
*.smod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app

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{
"files.associations": {
"complex": "cpp"
}
}

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{
"files.associations": {
"iosfwd": "cpp",
"core": "cpp",
"cctype": "cpp",
"clocale": "cpp",
"cmath": "cpp",
"cstdarg": "cpp",
"cstddef": "cpp",
"cstdio": "cpp",
"cstdlib": "cpp",
"cstring": "cpp",
"ctime": "cpp",
"cwchar": "cpp",
"cwctype": "cpp",
"array": "cpp",
"atomic": "cpp",
"strstream": "cpp",
"*.tcc": "cpp",
"bitset": "cpp",
"chrono": "cpp",
"complex": "cpp",
"cstdint": "cpp",
"deque": "cpp",
"list": "cpp",
"unordered_map": "cpp",
"vector": "cpp",
"exception": "cpp",
"fstream": "cpp",
"functional": "cpp",
"initializer_list": "cpp",
"iomanip": "cpp",
"iostream": "cpp",
"istream": "cpp",
"limits": "cpp",
"new": "cpp",
"ostream": "cpp",
"numeric": "cpp",
"ratio": "cpp",
"sstream": "cpp",
"stdexcept": "cpp",
"streambuf": "cpp",
"system_error": "cpp",
"thread": "cpp",
"cfenv": "cpp",
"cinttypes": "cpp",
"tuple": "cpp",
"type_traits": "cpp",
"utility": "cpp",
"typeinfo": "cpp",
"algorithm": "cpp",
"iterator": "cpp",
"map": "cpp",
"memory": "cpp",
"memory_resource": "cpp",
"optional": "cpp",
"random": "cpp",
"set": "cpp",
"string": "cpp",
"string_view": "cpp"
}
}

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src/Point-LIO/include/IKFoM/IKFoM_toolkit/esekfom/.vscode/settings.json vendored Executable file
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{
"files.associations": {
"core": "cpp"
}
}

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/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Author: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef ESEKFOM_EKF_HPP
#define ESEKFOM_EKF_HPP
#include <vector>
#include <cstdlib>
#include <boost/bind.hpp>
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <Eigen/Dense>
#include <Eigen/Eigen>
#include <Eigen/Sparse>
#include "../mtk/types/vect.hpp"
#include "../mtk/types/SOn.hpp"
#include "../mtk/types/S2.hpp"
#include "../mtk/types/SEn.hpp"
#include "../mtk/startIdx.hpp"
#include "../mtk/build_manifold.hpp"
#include "util.hpp"
namespace esekfom {
using namespace Eigen;
template<typename T>
struct dyn_share_modified
{
bool valid;
bool converge;
T M_Noise;
Eigen::Matrix<T, Eigen::Dynamic, 1> z;
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> h_x;
Eigen::Matrix<T, 6, 1> z_IMU;
Eigen::Matrix<T, 6, 1> R_IMU;
bool satu_check[6];
};
template<typename state, int process_noise_dof, typename input = state, typename measurement=state, int measurement_noise_dof=0>
class esekf{
typedef esekf self;
enum{
n = state::DOF, m = state::DIM, l = measurement::DOF
};
public:
typedef typename state::scalar scalar_type;
typedef Matrix<scalar_type, n, n> cov;
typedef Matrix<scalar_type, m, n> cov_;
typedef SparseMatrix<scalar_type> spMt;
typedef Matrix<scalar_type, n, 1> vectorized_state;
typedef Matrix<scalar_type, m, 1> flatted_state;
typedef flatted_state processModel(state &, const input &);
typedef Eigen::Matrix<scalar_type, m, n> processMatrix1(state &, const input &);
typedef Eigen::Matrix<scalar_type, m, process_noise_dof> processMatrix2(state &, const input &);
typedef Eigen::Matrix<scalar_type, process_noise_dof, process_noise_dof> processnoisecovariance;
typedef void measurementModel_dyn_share_modified(state &, dyn_share_modified<scalar_type> &);
typedef Eigen::Matrix<scalar_type ,l, n> measurementMatrix1(state &);
typedef Eigen::Matrix<scalar_type , Eigen::Dynamic, n> measurementMatrix1_dyn(state &);
typedef Eigen::Matrix<scalar_type ,l, measurement_noise_dof> measurementMatrix2(state &);
typedef Eigen::Matrix<scalar_type ,Eigen::Dynamic, Eigen::Dynamic> measurementMatrix2_dyn(state &);
typedef Eigen::Matrix<scalar_type, measurement_noise_dof, measurement_noise_dof> measurementnoisecovariance;
typedef Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> measurementnoisecovariance_dyn;
esekf(const state &x = state(),
const cov &P = cov::Identity()): x_(x), P_(P){};
void init_dyn_share_modified(processModel f_in, processMatrix1 f_x_in, measurementModel_dyn_share_modified h_dyn_share_in)
{
f = f_in;
f_x = f_x_in;
// f_w = f_w_in;
h_dyn_share_modified_1 = h_dyn_share_in;
maximum_iter = 1;
x_.build_S2_state();
x_.build_SO3_state();
x_.build_vect_state();
x_.build_SEN_state();
}
void init_dyn_share_modified_2h(processModel f_in, processMatrix1 f_x_in, measurementModel_dyn_share_modified h_dyn_share_in1, measurementModel_dyn_share_modified h_dyn_share_in2)
{
f = f_in;
f_x = f_x_in;
// f_w = f_w_in;
h_dyn_share_modified_1 = h_dyn_share_in1;
h_dyn_share_modified_2 = h_dyn_share_in2;
maximum_iter = 1;
x_.build_S2_state();
x_.build_SO3_state();
x_.build_vect_state();
x_.build_SEN_state();
}
// iterated error state EKF propogation
void predict(double &dt, processnoisecovariance &Q, const input &i_in, bool predict_state, bool prop_cov){
if (predict_state)
{
flatted_state f_ = f(x_, i_in);
x_.oplus(f_, dt);
}
if (prop_cov)
{
flatted_state f_ = f(x_, i_in);
// state x_before = x_;
cov_ f_x_ = f_x(x_, i_in);
cov f_x_final;
F_x1 = cov::Identity();
for (std::vector<std::pair<std::pair<int, int>, int> >::iterator it = x_.vect_state.begin(); it != x_.vect_state.end(); it++) {
int idx = (*it).first.first;
int dim = (*it).first.second;
int dof = (*it).second;
for(int i = 0; i < n; i++){
for(int j=0; j<dof; j++)
{f_x_final(idx+j, i) = f_x_(dim+j, i);}
}
}
Matrix<scalar_type, 3, 3> res_temp_SO3;
MTK::vect<3, scalar_type> seg_SO3;
for (std::vector<std::pair<int, int> >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) {
int idx = (*it).first;
int dim = (*it).second;
for(int i = 0; i < 3; i++){
seg_SO3(i) = -1 * f_(dim + i) * dt;
}
// MTK::SO3<scalar_type> res;
// res.w() = MTK::exp<scalar_type, 3>(res.vec(), seg_SO3, scalar_type(1/2));
F_x1.template block<3, 3>(idx, idx) = MTK::SO3<scalar_type>::exp(seg_SO3); // res.normalized().toRotationMatrix();
res_temp_SO3 = MTK::A_matrix(seg_SO3);
for(int i = 0; i < n; i++){
f_x_final. template block<3, 1>(idx, i) = res_temp_SO3 * (f_x_. template block<3, 1>(dim, i));
}
}
F_x1 += f_x_final * dt;
P_ = F_x1 * P_ * (F_x1).transpose() + Q * (dt * dt);
}
}
bool update_iterated_dyn_share_modified() {
dyn_share_modified<scalar_type> dyn_share;
state x_propagated = x_;
int dof_Measurement;
double m_noise;
for(int i=0; i<maximum_iter; i++)
{
dyn_share.valid = true;
h_dyn_share_modified_1(x_, dyn_share);
if(! dyn_share.valid)
{
return false;
// continue;
}
Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> z = dyn_share.z;
// Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> R = dyn_share.R;
Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> h_x = dyn_share.h_x;
// Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> h_v = dyn_share.h_v;
dof_Measurement = h_x.rows();
m_noise = dyn_share.M_Noise;
// dof_Measurement_noise = dyn_share.R.rows();
// vectorized_state dx, dx_new;
// x_.boxminus(dx, x_propagated);
// dx_new = dx;
// P_ = P_propagated;
Matrix<scalar_type, n, Eigen::Dynamic> PHT;
Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> HPHT;
Matrix<scalar_type, n, Eigen::Dynamic> K_;
// if(n > dof_Measurement)
{
PHT = P_. template block<n, 12>(0, 0) * h_x.transpose();
HPHT = h_x * PHT.topRows(12);
for (int m = 0; m < dof_Measurement; m++)
{
HPHT(m, m) += m_noise;
}
K_= PHT*HPHT.inverse();
}
Matrix<scalar_type, n, 1> dx_ = K_ * z; // - h) + (K_x - Matrix<scalar_type, n, n>::Identity()) * dx_new;
// state x_before = x_;
x_.boxplus(dx_);
dyn_share.converge = true;
// L_ = P_;
// Matrix<scalar_type, 3, 3> res_temp_SO3;
// MTK::vect<3, scalar_type> seg_SO3;
// for(typename std::vector<std::pair<int, int> >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) {
// int idx = (*it).first;
// for(int i = 0; i < 3; i++){
// seg_SO3(i) = dx_(i + idx);
// }
// res_temp_SO3 = A_matrix(seg_SO3).transpose();
// for(int i = 0; i < n; i++){
// L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i));
// }
// {
// for(int i = 0; i < dof_Measurement; i++){
// K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i));
// }
// }
// for(int i = 0; i < n; i++){
// L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose();
// // P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose();
// }
// for(int i = 0; i < n; i++){
// P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose();
// }
// }
// Matrix<scalar_type, 2, 2> res_temp_S2;
// MTK::vect<2, scalar_type> seg_S2;
// for(typename std::vector<std::pair<int, int> >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) {
// int idx = (*it).first;
// for(int i = 0; i < 2; i++){
// seg_S2(i) = dx_(i + idx);
// }
// Eigen::Matrix<scalar_type, 2, 3> Nx;
// Eigen::Matrix<scalar_type, 3, 2> Mx;
// x_.S2_Nx_yy(Nx, idx);
// x_propagated.S2_Mx(Mx, seg_S2, idx);
// res_temp_S2 = Nx * Mx;
// for(int i = 0; i < n; i++){
// L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i));
// }
// {
// for(int i = 0; i < dof_Measurement; i++){
// K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i));
// }
// }
// for(int i = 0; i < n; i++){
// L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose();
// }
// for(int i = 0; i < n; i++){
// P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose();
// }
// }
// if(n > dof_Measurement)
{
P_ = P_ - K_*h_x*P_. template block<12, n>(0, 0);
}
}
return true;
}
void update_iterated_dyn_share_IMU() {
dyn_share_modified<scalar_type> dyn_share;
for(int i=0; i<maximum_iter; i++)
{
dyn_share.valid = true;
h_dyn_share_modified_2(x_, dyn_share);
Matrix<scalar_type, 6, 1> z = dyn_share.z_IMU;
Matrix<double, 30, 6> PHT;
Matrix<double, 6, 30> HP;
Matrix<double, 6, 6> HPHT;
PHT.setZero();
HP.setZero();
HPHT.setZero();
for (int l_ = 0; l_ < 6; l_++)
{
if (!dyn_share.satu_check[l_])
{
PHT.col(l_) = P_.col(15+l_) + P_.col(24+l_);
HP.row(l_) = P_.row(15+l_) + P_.row(24+l_);
}
}
for (int l_ = 0; l_ < 6; l_++)
{
if (!dyn_share.satu_check[l_])
{
HPHT.col(l_) = HP.col(15+l_) + HP.col(24+l_);
}
HPHT(l_, l_) += dyn_share.R_IMU(l_); //, l);
}
Eigen::Matrix<double, 30, 6> K = PHT * HPHT.inverse();
Matrix<scalar_type, n, 1> dx_ = K * z;
P_ -= K * HP;
x_.boxplus(dx_);
}
return;
}
void change_x(state &input_state)
{
x_ = input_state;
if((!x_.vect_state.size())&&(!x_.SO3_state.size())&&(!x_.S2_state.size())&&(!x_.SEN_state.size()))
{
x_.build_S2_state();
x_.build_SO3_state();
x_.build_vect_state();
x_.build_SEN_state();
}
}
void change_P(cov &input_cov)
{
P_ = input_cov;
}
const state& get_x() const {
return x_;
}
const cov& get_P() const {
return P_;
}
state x_;
private:
measurement m_;
cov P_;
spMt l_;
spMt f_x_1;
spMt f_x_2;
cov F_x1 = cov::Identity();
cov F_x2 = cov::Identity();
cov L_ = cov::Identity();
processModel *f;
processMatrix1 *f_x;
processMatrix2 *f_w;
measurementMatrix1 *h_x;
measurementMatrix2 *h_v;
measurementMatrix1_dyn *h_x_dyn;
measurementMatrix2_dyn *h_v_dyn;
measurementModel_dyn_share_modified *h_dyn_share_modified_1;
measurementModel_dyn_share_modified *h_dyn_share_modified_2;
int maximum_iter = 0;
scalar_type limit[n];
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace esekfom
#endif // ESEKFOM_EKF_HPP

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/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Author: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef __MEKFOM_UTIL_HPP__
#define __MEKFOM_UTIL_HPP__
#include <Eigen/Core>
#include "../mtk/src/mtkmath.hpp"
namespace esekfom {
template <typename T1, typename T2>
class is_same {
public:
operator bool() {
return false;
}
};
template<typename T1>
class is_same<T1, T1> {
public:
operator bool() {
return true;
}
};
template <typename T>
class is_double {
public:
operator bool() {
return false;
}
};
template<>
class is_double<double> {
public:
operator bool() {
return true;
}
};
template<typename T>
static T
id(const T &x)
{
return x;
}
} // namespace esekfom
#endif // __MEKFOM_UTIL_HPP__

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/build_manifold.hpp
* @brief Macro to automatically construct compound manifolds.
*
*/
#ifndef MTK_AUTOCONSTRUCT_HPP_
#define MTK_AUTOCONSTRUCT_HPP_
#include <vector>
#include <boost/preprocessor/seq.hpp>
#include <boost/preprocessor/cat.hpp>
#include <Eigen/Core>
#include "src/SubManifold.hpp"
#include "startIdx.hpp"
#ifndef PARSED_BY_DOXYGEN
//////// internals //////
#define MTK_APPLY_MACRO_ON_TUPLE(r, macro, tuple) macro tuple
#define MTK_TRANSFORM_COMMA(macro, entries) BOOST_PP_SEQ_ENUM(BOOST_PP_SEQ_TRANSFORM_S(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries))
#define MTK_TRANSFORM(macro, entries) BOOST_PP_SEQ_FOR_EACH_R(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries)
#define MTK_CONSTRUCTOR_ARG( type, id) const type& id = type()
#define MTK_CONSTRUCTOR_COPY( type, id) id(id)
#define MTK_BOXPLUS( type, id) id.boxplus(MTK::subvector(__vec, &self::id), __scale);
#define MTK_OPLUS( type, id) id.oplus(MTK::subvector_(__vec, &self::id), __scale);
#define MTK_BOXMINUS( type, id) id.boxminus(MTK::subvector(__res, &self::id), __oth.id);
#define MTK_HAT( type, id) if(id.IDX == idx){id.hat(vec, res);}
#define MTK_JACOB_RIGHT_INV( type, id) if(id.IDX == idx){id.Jacob_right_inv(vec, res);}
#define MTK_JACOB_RIGHT( type, id) if(id.IDX == idx){id.Jacob_right(vec, res);}
#define MTK_S2_hat( type, id) if(id.IDX == idx){id.S2_hat(res);}
#define MTK_S2_Nx_yy( type, id) if(id.IDX == idx){id.S2_Nx_yy(res);}
#define MTK_S2_Mx( type, id) if(id.IDX == idx){id.S2_Mx(res, dx);}
#define MTK_OSTREAM( type, id) << __var.id << " "
#define MTK_ISTREAM( type, id) >> __var.id
#define MTK_S2_state( type, id) if(id.TYP == 1){S2_state.push_back(std::make_pair(id.IDX, id.DIM));}
#define MTK_SO3_state( type, id) if(id.TYP == 2){(SO3_state).push_back(std::make_pair(id.IDX, id.DIM));}
#define MTK_vect_state( type, id) if(id.TYP == 0){(vect_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));}
#define MTK_SEN_state( type, id) if(id.TYP == 4){(SEN_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));}
#define MTK_SUBVARLIST(seq, S2state, SO3state, SENstate) \
BOOST_PP_FOR_1( \
( \
BOOST_PP_SEQ_SIZE(seq), \
BOOST_PP_SEQ_HEAD(seq), \
BOOST_PP_SEQ_TAIL(seq) (~), \
0,\
0,\
S2state,\
SO3state,\
SENstate ),\
MTK_ENTRIES_TEST, MTK_ENTRIES_NEXT, MTK_ENTRIES_OUTPUT)
#define MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state, SENstate) \
MTK::SubManifold<type, dof, dim> id;
#define MTK_PUT_TYPE_AND_ENUM(type, id, dof, dim, S2state, SO3state, SENstate) \
MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state, SENstate) \
enum {DOF = type::DOF + dof}; \
enum {DIM = type::DIM+dim}; \
typedef type::scalar scalar;
#define MTK_ENTRIES_OUTPUT(r, state) MTK_ENTRIES_OUTPUT_I state
#define MTK_ENTRIES_OUTPUT_I(s, head, seq, dof, dim, S2state, SO3state, SENstate) \
MTK_APPLY_MACRO_ON_TUPLE(~, \
BOOST_PP_IF(BOOST_PP_DEC(s), MTK_PUT_TYPE, MTK_PUT_TYPE_AND_ENUM), \
( BOOST_PP_TUPLE_REM_2 head, dof, dim, S2state, SO3state, SENstate))
#define MTK_ENTRIES_TEST(r, state) MTK_TUPLE_ELEM_4_0 state
//! this used to be BOOST_PP_TUPLE_ELEM_4_0:
#define MTK_TUPLE_ELEM_4_0(a,b,c,d,e,f, g, h) a
#define MTK_ENTRIES_NEXT(r, state) MTK_ENTRIES_NEXT_I state
#define MTK_ENTRIES_NEXT_I(len, head, seq, dof, dim, S2state, SO3state, SENstate) ( \
BOOST_PP_DEC(len), \
BOOST_PP_SEQ_HEAD(seq), \
BOOST_PP_SEQ_TAIL(seq), \
dof + BOOST_PP_TUPLE_ELEM_2_0 head::DOF,\
dim + BOOST_PP_TUPLE_ELEM_2_0 head::DIM,\
S2state,\
SO3state,\
SENstate )
#endif /* not PARSED_BY_DOXYGEN */
/**
* Construct a manifold.
* @param name is the class-name of the manifold,
* @param entries is the list of sub manifolds
*
* Entries must be given in a list like this:
* @code
* typedef MTK::trafo<MTK::SO3<double> > Pose;
* typedef MTK::vect<double, 3> Vec3;
* MTK_BUILD_MANIFOLD(imu_state,
* ((Pose, pose))
* ((Vec3, vel))
* ((Vec3, acc_bias))
* )
* @endcode
* Whitespace is optional, but the double parentheses are necessary.
* Construction is done entirely in preprocessor.
* After construction @a name is also a manifold. Its members can be
* accessed by names given in @a entries.
*
* @note Variable types are not allowed to have commas, thus types like
* @c vect<double, 3> need to be typedef'ed ahead.
*/
#define MTK_BUILD_MANIFOLD(name, entries) \
struct name { \
typedef name self; \
std::vector<std::pair<int, int> > S2_state;\
std::vector<std::pair<int, int> > SO3_state;\
std::vector<std::pair<std::pair<int, int>, int> > vect_state;\
std::vector<std::pair<std::pair<int, int>, int> > SEN_state;\
MTK_SUBVARLIST(entries, S2_state, SO3_state, SEN_state) \
name ( \
MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_ARG, entries) \
) : \
MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_COPY, entries) {}\
int getDOF() const { return DOF; } \
void boxplus(const MTK::vectview<const scalar, DOF> & __vec, scalar __scale = 1 ) { \
MTK_TRANSFORM(MTK_BOXPLUS, entries)\
} \
void oplus(const MTK::vectview<const scalar, DIM> & __vec, scalar __scale = 1 ) { \
MTK_TRANSFORM(MTK_OPLUS, entries)\
} \
void boxminus(MTK::vectview<scalar,DOF> __res, const name& __oth) const { \
MTK_TRANSFORM(MTK_BOXMINUS, entries)\
} \
friend std::ostream& operator<<(std::ostream& __os, const name& __var){ \
return __os MTK_TRANSFORM(MTK_OSTREAM, entries); \
} \
void build_S2_state(){\
MTK_TRANSFORM(MTK_S2_state, entries)\
}\
void build_vect_state(){\
MTK_TRANSFORM(MTK_vect_state, entries)\
}\
void build_SO3_state(){\
MTK_TRANSFORM(MTK_SO3_state, entries)\
}\
void build_SEN_state(){\
MTK_TRANSFORM(MTK_SEN_state, entries)\
}\
void Lie_hat(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\
MTK_TRANSFORM(MTK_HAT, entries)\
}\
void Lie_Jacob_Right_Inv(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\
MTK_TRANSFORM(MTK_JACOB_RIGHT_INV, entries)\
}\
void Lie_Jacob_Right(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\
MTK_TRANSFORM(MTK_JACOB_RIGHT, entries)\
}\
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res, int idx) {\
MTK_TRANSFORM(MTK_S2_hat, entries)\
}\
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res, int idx) {\
MTK_TRANSFORM(MTK_S2_Nx_yy, entries)\
}\
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, Eigen::Matrix<scalar, 2, 1> dx, int idx) {\
MTK_TRANSFORM(MTK_S2_Mx, entries)\
}\
friend std::istream& operator>>(std::istream& __is, name& __var){ \
return __is MTK_TRANSFORM(MTK_ISTREAM, entries); \
} \
};
#endif /*MTK_AUTOCONSTRUCT_HPP_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/SubManifold.hpp
* @brief Defines the SubManifold class
*/
#ifndef SUBMANIFOLD_HPP_
#define SUBMANIFOLD_HPP_
#include "vectview.hpp"
namespace MTK {
/**
* @ingroup SubManifolds
* Helper class for compound manifolds.
* This class wraps a manifold T and provides an enum IDX refering to the
* index of the SubManifold within the compound manifold.
*
* Memberpointers to a submanifold can be used for @ref SubManifolds "functions accessing submanifolds".
*
* @tparam T The manifold type of the sub-type
* @tparam idx The index of the sub-type within the compound manifold
*/
template<class T, int idx, int dim>
struct SubManifold : public T
{
enum {IDX = idx, DIM = dim /*!< index of the sub-type within the compound manifold */ };
//! manifold type
typedef T type;
//! Construct from derived type
template<class X>
explicit
SubManifold(const X& t) : T(t) {};
//! Construct from internal type
//explicit
SubManifold(const T& t) : T(t) {};
//! inherit assignment operator
using T::operator=;
};
} // namespace MTK
#endif /* SUBMANIFOLD_HPP_ */

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/mtkmath.hpp
* @brief several math utility functions.
*/
#ifndef MTKMATH_H_
#define MTKMATH_H_
#include <cmath>
#include <boost/math/tools/precision.hpp>
#include "../types/vect.hpp"
#ifndef M_PI
#define M_PI 3.1415926535897932384626433832795
#endif
namespace MTK {
namespace internal {
template<class Manifold>
struct traits {
typedef typename Manifold::scalar scalar;
enum {DOF = Manifold::DOF};
typedef vect<DOF, scalar> vectorized_type;
typedef Eigen::Matrix<scalar, DOF, DOF> matrix_type;
};
template<>
struct traits<float> : traits<Scalar<float> > {};
template<>
struct traits<double> : traits<Scalar<double> > {};
} // namespace internal
/**
* \defgroup MTKMath Mathematical helper functions
*/
//@{
//! constant @f$ \pi @f$
const double pi = M_PI;
template<class scalar> inline scalar tolerance();
template<> inline float tolerance<float >() { return 1e-5f; }
template<> inline double tolerance<double>() { return 1e-11; }
/**
* normalize @a x to @f$[-bound, bound] @f$.
*
* result for @f$ x = bound + 2\cdot n\cdot bound @f$ is arbitrary @f$\pm bound @f$.
*/
template<class scalar>
inline scalar normalize(scalar x, scalar bound){ //not used
if(std::fabs(x) <= bound) return x;
int r = (int)(x *(scalar(1.0)/ bound));
return x - ((r + (r>>31) + 1) & ~1)*bound;
}
/**
* Calculate cosine and sinc of sqrt(x2).
* @param x2 the squared angle must be non-negative
* @return a pair containing cos and sinc of sqrt(x2)
*/
template<class scalar>
std::pair<scalar, scalar> cos_sinc_sqrt(const scalar &x2){
using std::sqrt;
using std::cos;
using std::sin;
static scalar const taylor_0_bound = boost::math::tools::epsilon<scalar>();
static scalar const taylor_2_bound = sqrt(taylor_0_bound);
static scalar const taylor_n_bound = sqrt(taylor_2_bound);
assert(x2>=0 && "argument must be non-negative and must not be nan/-nan");
// FIXME check if bigger bounds are possible
if(x2>=taylor_n_bound) {
// slow fall-back solution
scalar x = sqrt(x2);
return std::make_pair(cos(x), sin(x)/x); // x is greater than 0.
}
// FIXME Replace by Horner-Scheme (4 instead of 5 FLOP/term, numerically more stable, theoretically cos and sinc can be calculated in parallel using SSE2 mulpd/addpd)
// TODO Find optimal coefficients using Remez algorithm
static scalar const inv[] = {1/3., 1/4., 1/5., 1/6., 1/7., 1/8., 1/9.};
scalar cosi = 1., sinc=1;
scalar term = -1/2. * x2;
for(int i=0; i<3; ++i) {
cosi += term;
term *= inv[2*i];
sinc += term;
term *= -inv[2*i+1] * x2;
}
return std::make_pair(cosi, sinc);
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> hat(const Base& v) {
Eigen::Matrix<typename Base::scalar, 3, 3> res;
res << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
return res;
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_inv_trans(const Base& v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
if(v.norm() > MTK::tolerance<typename Base::scalar>())
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() + 0.5 * hat<Base>(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm();
}
else
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
return res;
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_inv(const Base& v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
if(v.norm() > MTK::tolerance<typename Base::scalar>())
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() - 0.5 * hat<Base>(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm();
}
else
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
return res;
}
template<typename scalar>
Eigen::Matrix<scalar, 2, 3> S2_w_expw_( Eigen::Matrix<scalar, 2, 1> v, scalar length)
{
Eigen::Matrix<scalar, 2, 3> res;
scalar norm = std::sqrt(v[0]*v[0] + v[1]*v[1]);
if(norm < MTK::tolerance<scalar>()){
res = Eigen::Matrix<scalar, 2, 3>::Zero();
res(0, 1) = 1;
res(1, 2) = 1;
res /= length;
}
else{
res << -v[0]*(1/norm-1/std::tan(norm))/std::sin(norm), norm/std::sin(norm), 0,
-v[1]*(1/norm-1/std::tan(norm))/std::sin(norm), 0, norm/std::sin(norm);
res /= length;
}
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_matrix(const Base & v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
double norm = std::sqrt(squaredNorm);
if(norm < MTK::tolerance<typename Base::scalar>()){
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
else{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() + (1 - std::cos(norm)) / squaredNorm * hat(v) + (1 - std::sin(norm) / norm) / squaredNorm * hat(v) * hat(v);
}
return res;
}
template<class scalar, int n>
scalar exp(vectview<scalar, n> result, vectview<const scalar, n> vec, const scalar& scale = 1) {
scalar norm2 = vec.squaredNorm();
std::pair<scalar, scalar> cos_sinc = cos_sinc_sqrt(scale*scale * norm2);
scalar mult = cos_sinc.second * scale;
result = mult * vec;
return cos_sinc.first;
}
/**
* Inverse function to @c exp.
*
* @param result @c vectview to the result
* @param w scalar part of input
* @param vec vector part of input
* @param scale scale result by this value
* @param plus_minus_periodicity if true values @f$[w, vec]@f$ and @f$[-w, -vec]@f$ give the same result
*/
template<class scalar, int n>
void log(vectview<scalar, n> result,
const scalar &w, const vectview<const scalar, n> vec,
const scalar &scale, bool plus_minus_periodicity)
{
// FIXME implement optimized case for vec.squaredNorm() <= tolerance() * (w*w) via Rational Remez approximation ~> only one division
scalar nv = vec.norm();
if(nv < tolerance<scalar>()) {
if(!plus_minus_periodicity && w < 0) {
// find the maximal entry:
int i;
nv = vec.cwiseAbs().maxCoeff(&i);
result = scale * std::atan2(nv, w) * vect<n, scalar>::Unit(i);
return;
}
nv = tolerance<scalar>();
}
scalar s = scale / nv * (plus_minus_periodicity ? std::atan(nv / w) : std::atan2(nv, w) );
result = s * vec;
}
} // namespace MTK
#endif /* MTKMATH_H_ */

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/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/vectview.hpp
* @brief Wrapper class around a pointer used as interface for plain vectors.
*/
#ifndef VECTVIEW_HPP_
#define VECTVIEW_HPP_
#include <Eigen/Core>
namespace MTK {
/**
* A view to a vector.
* Essentially, @c vectview is only a pointer to @c scalar but can be used directly in @c Eigen expressions.
* The dimension of the vector is given as template parameter and type-checked when used in expressions.
* Data has to be modifiable.
*
* @tparam scalar Scalar type of the vector.
* @tparam dim Dimension of the vector.
*
* @todo @c vectview can be replaced by simple inheritance of @c Eigen::Map, as soon as they get const-correct
*/
namespace internal {
template<class Base, class T1, class T2>
struct CovBlock {
typedef typename Eigen::Block<Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF>, T1::DOF, T2::DOF> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF>, T1::DOF, T2::DOF> ConstType;
};
template<class Base, class T1, class T2>
struct CovBlock_ {
typedef typename Eigen::Block<Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM>, T1::DIM, T2::DIM> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM>, T1::DIM, T2::DIM> ConstType;
};
template<typename Base1, typename Base2, typename T1, typename T2>
struct CrossCovBlock {
typedef typename Eigen::Block<Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF>, T1::DOF, T2::DOF> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF>, T1::DOF, T2::DOF> ConstType;
};
template<typename Base1, typename Base2, typename T1, typename T2>
struct CrossCovBlock_ {
typedef typename Eigen::Block<Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM>, T1::DIM, T2::DIM> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM>, T1::DIM, T2::DIM> ConstType;
};
template<class scalar, int dim>
struct VectviewBase {
typedef Eigen::Matrix<scalar, dim, 1> matrix_type;
typedef typename matrix_type::MapType Type;
typedef typename matrix_type::ConstMapType ConstType;
};
template<class T>
struct UnalignedType {
typedef T type;
};
}
template<class scalar, int dim>
class vectview : public internal::VectviewBase<scalar, dim>::Type {
typedef internal::VectviewBase<scalar, dim> VectviewBase;
public:
//! plain matrix type
typedef typename VectviewBase::matrix_type matrix_type;
//! base type
typedef typename VectviewBase::Type base;
//! construct from pointer
explicit
vectview(scalar* data, int dim_=dim) : base(data, dim_) {}
//! construct from plain matrix
vectview(matrix_type& m) : base(m.data(), m.size()) {}
//! construct from another @c vectview
vectview(const vectview &v) : base(v) {}
//! construct from Eigen::Block:
template<class Base>
vectview(Eigen::VectorBlock<Base, dim> block) : base(&block.coeffRef(0), block.size()) {}
template<class Base, bool PacketAccess>
vectview(Eigen::Block<Base, dim, 1, PacketAccess> block) : base(&block.coeffRef(0), block.size()) {}
//! inherit assignment operator
using base::operator=;
//! data pointer
scalar* data() {return const_cast<scalar*>(base::data());}
};
/**
* @c const version of @c vectview.
* Compared to @c Eigen::Map this implementation is const correct, i.e.,
* data will not be modifiable using this view.
*
* @tparam scalar Scalar type of the vector.
* @tparam dim Dimension of the vector.
*
* @sa vectview
*/
template<class scalar, int dim>
class vectview<const scalar, dim> : public internal::VectviewBase<scalar, dim>::ConstType {
typedef internal::VectviewBase<scalar, dim> VectviewBase;
public:
//! plain matrix type
typedef typename VectviewBase::matrix_type matrix_type;
//! base type
typedef typename VectviewBase::ConstType base;
//! construct from const pointer
explicit
vectview(const scalar* data, int dim_ = dim) : base(data, dim_) {}
//! construct from column vector
template<int options>
vectview(const Eigen::Matrix<scalar, dim, 1, options>& m) : base(m.data()) {}
//! construct from row vector
template<int options, int phony>
vectview(const Eigen::Matrix<scalar, 1, dim, options, phony>& m) : base(m.data()) {}
//! construct from another @c vectview
vectview(vectview<scalar, dim> x) : base(x.data()) {}
//! construct from base
vectview(const base &x) : base(x) {}
/**
* Construct from Block
* @todo adapt this, when Block gets const-correct
*/
template<class Base>
vectview(Eigen::VectorBlock<Base, dim> block) : base(&block.coeffRef(0)) {}
template<class Base, bool PacketAccess>
vectview(Eigen::Block<Base, dim, 1, PacketAccess> block) : base(&block.coeffRef(0)) {}
};
} // namespace MTK
#endif /* VECTVIEW_HPP_ */

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/startIdx.hpp
* @brief Tools to access sub-elements of compound manifolds.
*/
#ifndef GET_START_INDEX_H_
#define GET_START_INDEX_H_
#include <Eigen/Core>
#include "src/SubManifold.hpp"
#include "src/vectview.hpp"
namespace MTK {
/**
* \defgroup SubManifolds Accessing Submanifolds
* For compound manifolds constructed using MTK_BUILD_MANIFOLD, member pointers
* can be used to get sub-vectors or matrix-blocks of a corresponding big matrix.
* E.g. for a type @a pose consisting of @a orient and @a trans the member pointers
* @c &pose::orient and @c &pose::trans give all required information and are still
* valid if the base type gets extended or the actual types of @a orient and @a trans
* change (e.g. from 2D to 3D).
*
* @todo Maybe require manifolds to typedef MatrixType and VectorType, etc.
*/
//@{
/**
* Determine the index of a sub-variable within a compound variable.
*/
template<class Base, class T, int idx, int dim>
int getStartIdx( MTK::SubManifold<T, idx, dim> Base::*)
{
return idx;
}
template<class Base, class T, int idx, int dim>
int getStartIdx_( MTK::SubManifold<T, idx, dim> Base::*)
{
return dim;
}
/**
* Determine the degrees of freedom of a sub-variable within a compound variable.
*/
template<class Base, class T, int idx, int dim>
int getDof( MTK::SubManifold<T, idx, dim> Base::*)
{
return T::DOF;
}
template<class Base, class T, int idx, int dim>
int getDim( MTK::SubManifold<T, idx, dim> Base::*)
{
return T::DIM;
}
/**
* set the diagonal elements of a covariance matrix corresponding to a sub-variable
*/
template<class Base, class T, int idx, int dim>
void setDiagonal(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T, idx, dim> Base::*, const typename Base::scalar &val)
{
cov.diagonal().template segment<T::DOF>(idx).setConstant(val);
}
template<class Base, class T, int idx, int dim>
void setDiagonal_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T, idx, dim> Base::*, const typename Base::scalar &val)
{
cov.diagonal().template segment<T::DIM>(dim).setConstant(val);
}
/**
* Get the subblock of corresponding to two members, i.e.
* \code
* Eigen::Matrix<double, Pose::DOF, Pose::DOF> m;
* MTK::subblock(m, &Pose::orient, &Pose::trans) = some_expression;
* MTK::subblock(m, &Pose::trans, &Pose::orient) = some_expression.trans();
* \endcode
* lets you modify mixed covariance entries in a bigger covariance matrix.
*/
template<class Base, class T1, int idx1, int dim1, class T2, int idx2, int dim2>
typename MTK::internal::CovBlock<Base, T1, T2>::Type
subblock(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T1, idx1, dim1> Base::*, MTK::SubManifold<T2, idx2, dim2> Base::*)
{
return cov.template block<T1::DOF, T2::DOF>(idx1, idx2);
}
template<class Base, class T1, int idx1, int dim1, class T2, int idx2, int dim2>
typename MTK::internal::CovBlock_<Base, T1, T2>::Type
subblock_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T1, idx1, dim1> Base::*, MTK::SubManifold<T2, idx2, dim2> Base::*)
{
return cov.template block<T1::DIM, T2::DIM>(dim1, dim2);
}
template<typename Base1, typename Base2, typename T1, typename T2, int idx1, int idx2, int dim1, int dim2>
typename MTK::internal::CrossCovBlock<Base1, Base2, T1, T2>::Type
subblock(Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF> &cov, MTK::SubManifold<T1, idx1, dim1> Base1::*, MTK::SubManifold<T2, idx2, dim2> Base2::*)
{
return cov.template block<T1::DOF, T2::DOF>(idx1, idx2);
}
template<typename Base1, typename Base2, typename T1, typename T2, int idx1, int idx2, int dim1, int dim2>
typename MTK::internal::CrossCovBlock_<Base1, Base2, T1, T2>::Type
subblock_(Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM> &cov, MTK::SubManifold<T1, idx1, dim1> Base1::*, MTK::SubManifold<T2, idx2, dim2> Base2::*)
{
return cov.template block<T1::DIM, T2::DIM>(dim1, dim2);
}
/**
* Get the subblock of corresponding to a member, i.e.
* \code
* Eigen::Matrix<double, Pose::DOF, Pose::DOF> m;
* MTK::subblock(m, &Pose::orient) = some_expression;
* \endcode
* lets you modify covariance entries in a bigger covariance matrix.
*/
template<class Base, class T, int idx, int dim>
typename MTK::internal::CovBlock_<Base, T, T>::Type
subblock_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T, idx, dim> Base::*)
{
return cov.template block<T::DIM, T::DIM>(dim, dim);
}
template<class Base, class T, int idx, int dim>
typename MTK::internal::CovBlock<Base, T, T>::Type
subblock(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T, idx, dim> Base::*)
{
return cov.template block<T::DOF, T::DOF>(idx, idx);
}
template<typename Base>
class get_cov {
public:
typedef Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> type;
typedef const Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> const_type;
};
template<typename Base>
class get_cov_ {
public:
typedef Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> type;
typedef const Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> const_type;
};
template<typename Base1, typename Base2>
class get_cross_cov {
public:
typedef Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF> type;
typedef const type const_type;
};
template<typename Base1, typename Base2>
class get_cross_cov_ {
public:
typedef Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM> type;
typedef const type const_type;
};
template<class Base, class T, int idx, int dim>
vectview<typename Base::scalar, T::DIM>
subvector_impl_(vectview<typename Base::scalar, Base::DIM> vec, SubManifold<T, idx, dim> Base::*)
{
return vec.template segment<T::DIM>(dim);
}
template<class Base, class T, int idx, int dim>
vectview<typename Base::scalar, T::DOF>
subvector_impl(vectview<typename Base::scalar, Base::DOF> vec, SubManifold<T, idx, dim> Base::*)
{
return vec.template segment<T::DOF>(idx);
}
/**
* Get the subvector corresponding to a sub-manifold from a bigger vector.
*/
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<Scalar, T::DIM>
subvector_(vectview<Scalar, BaseDIM> vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vec, ptr);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<Scalar, T::DOF>
subvector(vectview<Scalar, BaseDOF> vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vec, ptr);
}
/**
* @todo This should be covered already by subvector(vectview<typename Base::scalar,Base::DOF> vec,SubManifold<T,idx> Base::*)
*/
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<Scalar, T::DOF>
subvector(Eigen::Matrix<Scalar, BaseDOF, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vectview<Scalar, BaseDOF>(vec), ptr);
}
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<Scalar, T::DIM>
subvector_(Eigen::Matrix<Scalar, BaseDIM, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vectview<Scalar, BaseDIM>(vec), ptr);
}
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DIM>
subvector_(const Eigen::Matrix<Scalar, BaseDIM, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vectview<const Scalar, BaseDIM>(vec), ptr);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DOF>
subvector(const Eigen::Matrix<Scalar, BaseDOF, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vectview<const Scalar, BaseDOF>(vec), ptr);
}
/**
* const version of subvector(vectview<typename Base::scalar,Base::DOF> vec,SubManifold<T,idx> Base::*)
*/
template<class Base, class T, int idx, int dim>
vectview<const typename Base::scalar, T::DOF>
subvector_impl(const vectview<const typename Base::scalar, Base::DOF> cvec, SubManifold<T, idx, dim> Base::*)
{
return cvec.template segment<T::DOF>(idx);
}
template<class Base, class T, int idx, int dim>
vectview<const typename Base::scalar, T::DIM>
subvector_impl_(const vectview<const typename Base::scalar, Base::DIM> cvec, SubManifold<T, idx, dim> Base::*)
{
return cvec.template segment<T::DIM>(dim);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DOF>
subvector(const vectview<const Scalar, BaseDOF> cvec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(cvec, ptr);
}
} // namespace MTK
#endif // GET_START_INDEX_H_

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// This is a NEW implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/S2.hpp
* @brief Unit vectors on the sphere, or directions in 3D.
*/
#ifndef S2_H_
#define S2_H_
#include "vect.hpp"
#include "SOn.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Manifold representation of @f$ S^2 @f$.
* Used for unit vectors on the sphere or directions in 3D.
*
* @todo add conversions from/to polar angles?
*/
template<class _scalar = double, int den = 1, int num = 1, int S2_typ = 3>
struct S2 {
typedef _scalar scalar;
typedef vect<3, scalar> vect_type;
typedef SO3<scalar> SO3_type;
typedef typename vect_type::base vec3;
scalar length = scalar(den)/scalar(num);
enum {DOF=2, TYP = 1, DIM = 3};
//private:
/**
* Unit vector on the sphere, or vector pointing in a direction
*/
vect_type vec;
public:
S2() {
if(S2_typ == 3) vec=length * vec3(0, 0, std::sqrt(1));
if(S2_typ == 2) vec=length * vec3(0, std::sqrt(1), 0);
if(S2_typ == 1) vec=length * vec3(std::sqrt(1), 0, 0);
}
S2(const scalar &x, const scalar &y, const scalar &z) : vec(vec3(x, y, z)) {
vec.normalize();
vec = vec * length;
}
S2(const vect_type &_vec) : vec(_vec) {
vec.normalize();
vec = vec * length;
}
void oplus(MTK::vectview<const scalar, 3> delta, scalar scale = 1)
{
SO3_type res;
res.w() = MTK::exp<scalar, 3>(res.vec(), delta, scalar(scale/2));
vec = res.normalized().toRotationMatrix() * vec;
}
void boxplus(MTK::vectview<const scalar, 2> delta, scalar scale=1) {
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
vect_type Bu = Bx*delta;SO3_type res;
res.w() = MTK::exp<scalar, 3>(res.vec(), Bu, scalar(scale/2));
vec = res.normalized().toRotationMatrix() * vec;
}
void boxminus(MTK::vectview<scalar, 2> res, const S2<scalar, den, num, S2_typ>& other) const {
scalar v_sin = (MTK::hat(vec)*other.vec).norm();
scalar v_cos = vec.transpose() * other.vec;
scalar theta = std::atan2(v_sin, v_cos);
if(v_sin < MTK::tolerance<scalar>())
{
if(std::fabs(theta) > MTK::tolerance<scalar>() )
{
res[0] = 3.1415926;
res[1] = 0;
}
else{
res[0] = 0;
res[1] = 0;
}
}
else
{
S2<scalar, den, num, S2_typ> other_copy = other;
Eigen::Matrix<scalar, 3, 2>Bx;
other_copy.S2_Bx(Bx);
res = theta/v_sin * Bx.transpose() * MTK::hat(other.vec)*vec;
}
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
Eigen::Matrix<scalar, 3, 3> skew_vec;
skew_vec << scalar(0), -vec[2], vec[1],
vec[2], scalar(0), -vec[0],
-vec[1], vec[0], scalar(0);
res = skew_vec;
}
void S2_Bx(Eigen::Matrix<scalar, 3, 2> &res)
{
if(S2_typ == 3)
{
if(vec[2] + length > tolerance<scalar>())
{
res << length - vec[0]*vec[0]/(length+vec[2]), -vec[0]*vec[1]/(length+vec[2]),
-vec[0]*vec[1]/(length+vec[2]), length-vec[1]*vec[1]/(length+vec[2]),
-vec[0], -vec[1];
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
else if(S2_typ == 2)
{
if(vec[1] + length > tolerance<scalar>())
{
res << length - vec[0]*vec[0]/(length+vec[1]), -vec[0]*vec[2]/(length+vec[1]),
-vec[0], -vec[2],
-vec[0]*vec[2]/(length+vec[1]), length-vec[2]*vec[2]/(length+vec[1]);
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
else
{
if(vec[0] + length > tolerance<scalar>())
{
res << -vec[1], -vec[2],
length - vec[1]*vec[1]/(length+vec[0]), -vec[2]*vec[1]/(length+vec[0]),
-vec[2]*vec[1]/(length+vec[0]), length-vec[2]*vec[2]/(length+vec[0]);
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
}
void S2_Nx(Eigen::Matrix<scalar, 2, 3> &res, S2<scalar, den, num, S2_typ>& subtrahend)
{
if((vec+subtrahend.vec).norm() > tolerance<scalar>())
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
if((vec-subtrahend.vec).norm() > tolerance<scalar>())
{
scalar v_sin = (MTK::hat(vec)*subtrahend.vec).norm();
scalar v_cos = vec.transpose() * subtrahend.vec;
res = Bx.transpose() * (std::atan2(v_sin, v_cos)/v_sin*MTK::hat(vec)+MTK::hat(vec)*subtrahend.vec*((-v_cos/v_sin/v_sin/length/length/length/length+std::atan2(v_sin, v_cos)/v_sin/v_sin/v_sin)*subtrahend.vec.transpose()*MTK::hat(vec)*MTK::hat(vec)-vec.transpose()/length/length/length/length));
}
else
{
res = 1/length/length*Bx.transpose()*MTK::hat(vec);
}
}
else
{
std::cerr << "No N(x, y) for x=-y" << std::endl;
std::exit(100);
}
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
res = 1/length/length*Bx.transpose()*MTK::hat(vec);
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
if(delta.norm() < tolerance<scalar>())
{
res = -MTK::hat(vec)*Bx;
}
else{
vect_type Bu = Bx*delta;
SO3_type exp_delta;
exp_delta.w() = MTK::exp<scalar, 3>(exp_delta.vec(), Bu, scalar(1/2));
res = -exp_delta.normalized().toRotationMatrix()*MTK::hat(vec)*MTK::A_matrix(Bu).transpose()*Bx;
}
}
operator const vect_type&() const{
return vec;
}
const vect_type& get_vect() const {
return vec;
}
friend S2<scalar, den, num, S2_typ> operator*(const SO3<scalar>& rot, const S2<scalar, den, num, S2_typ>& dir)
{
S2<scalar, den, num, S2_typ> ret;
ret.vec = rot.normalized() * dir.vec;
return ret;
}
scalar operator[](int idx) const {return vec[idx]; }
friend std::ostream& operator<<(std::ostream &os, const S2<scalar, den, num, S2_typ>& vec){
return os << vec.vec.transpose() << " ";
}
friend std::istream& operator>>(std::istream &is, S2<scalar, den, num, S2_typ>& vec){
for(int i=0; i<3; ++i)
is >> vec.vec[i];
vec.vec.normalize();
vec.vec = vec.vec * vec.length;
return is;
}
};
} // namespace MTK
#endif /*S2_H_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/SEn.hpp
* @brief Standard Orthogonal Groups i.e.\ rotatation groups.
*/
#ifndef SEN_H_
#define SEN_H_
#include <Eigen/Geometry>
#include "SOn.hpp"
#include "vect.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Three-dimensional orientations represented as Quaternion.
* It is assumed that the internal Quaternion always stays normalized,
* should this not be the case, call inherited member function @c normalize().
*/
template<class _scalar = double, int num_of_vec_plus1 = 6, int dim_of_mat = 4, int Options = Eigen::AutoAlign>
struct SEN {
enum {DOF = num_of_vec_plus1, DIM = num_of_vec_plus1, TYP = 4};
typedef _scalar scalar;
typedef Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> base;
typedef SO3<scalar> SO3_type;
// typedef Eigen::Quaternion<scalar, Options> base;
// typedef Eigen::Quaternion<scalar> Quaternion;
typedef vect<DIM, scalar, Options> vect_type;
SO3_type SO3_data;
base mat;
/**
* Construct from real part and three imaginary parts.
* Quaternion is normalized after construction.
*/
// SEN(const base& src) : mat(src) {
// // base::normalize();
// }
/**
* Construct from Eigen::Quaternion.
* @note Non-normalized input may result result in spurious behavior.
*/
SEN(const base& src = base::Identity()) : mat(src) {}
/**
* Construct from rotation matrix.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
// template<class Derived>
// SO3(const Eigen::MatrixBase<Derived>& matrix) : base(matrix) {}
/**
* Construct from arbitrary rotation type.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
// template<class Derived>
// SO3(const Eigen::RotationBase<Derived, 3>& rotation) : base(rotation.derived()) {}
//! @name Manifold requirements
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SEN delta = exp(vec, scale); // ?
mat = mat * delta.mat;
}
void boxminus(MTK::vectview<scalar, DOF> res, const SEN<scalar,num_of_vec_plus1,dim_of_mat, Options>& other) const {
base error_mat = other.mat.inverse() * mat;
res = log(error_mat);
}
//}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SEN delta = exp(vec, scale);
mat = mat * delta.mat;
}
// void hat(MTK::vectview<const scalar, DOF>& v, Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> &res) {
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
res = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Zero();
Eigen::Matrix<scalar, 3, 3> psi;
psi << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
res.block<3, 3>(0, 0) = psi;
for(int i = 3; i < v.size() / 3 + 2; i++)
{
res.block<3, 1>(0, i) = v.segment<3>(i + (i-3)*3);
}
// return res;
}
// void Jacob_right_inv(MTK::vectview<const scalar, DOF> vec, Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> & res){
void Jacob_right_inv(Eigen::VectorXd& vec, Eigen::MatrixXd &res){
res = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Zero();
Eigen::Matrix<scalar, 3, 3> M_v;
Eigen::VectorXd vec_psi, vec_ro;
Eigen::MatrixXd jac_v;
Eigen::MatrixXd hat_v, hat_ro;
vec_psi = vec.segment<3>(0);
// Eigen::Matrix<scalar, 3, 1> ;
SO3_data.hat(vec_psi, hat_v);
SO3_data.Jacob_right_inv(vec_psi, jac_v);
double norm = vec_psi.norm();
for(int i = 0; i < vec.size() / 3; i++)
{
res.block<3, 3>(i*3, i*3) = jac_v;
}
for(int i = 1; i < vec.size() / 3; i++)
{
vec_ro = vec.segment<3>(i * 3);
SO3_data.hat(vec_ro, hat_ro);
if(norm > MTK::tolerance<scalar>())
{
res.block<3,3>(i*3, 0) = 0.5 * hat_ro + (1 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2))/norm/norm * (hat_ro * hat_v + hat_v * hat_ro) + ((2 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2)) / 2 / norm / norm / norm / norm - 1 / 8 / norm / norm / std::sin(norm / 2) / std::sin(norm / 2)) * hat_v * (hat_ro * hat_v + hat_v * hat_ro) * hat_v;
}
else
{
res.block<3,3>(i*3, 0) = 0.5 * hat_ro;
}
}
// return res;
}
// void Jacob_right(MTK::vectview<const scalar, DOF> & vec, Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> &res){
void Jacob_right(Eigen::VectorXd& vec, Eigen::MatrixXd &res){
res = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Zero();
Eigen::MatrixXd hat_v, hat_ro;
Eigen::VectorXd vec_psi, vec_ro;
Eigen::MatrixXd jac_v;
vec_psi = vec.segment<3>(0);
// Eigen::Matrix<scalar, 3, 1> ;
SO3_data.hat(vec_psi, hat_v);
SO3_data.Jacob_right(vec_psi, jac_v);
// double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
// double norm = std::sqrt(squaredNorm);
double norm = vec_psi.norm();
for(int i = 0; i < vec.size() / 3; i++)
{
res.block<3, 3>(i*3, i*3) = jac_v;
}
for(int i = 1; i < vec.size() / 3; i++)
{
vec_ro = vec.segment<3>(i * 3);
SO3_data.hat(vec_ro, hat_ro);
if(norm > MTK::tolerance<scalar>())
{
res.block<3,3>(i*3, 0) = -1 * jac_v * (0.5 * hat_ro + (1 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2))/norm/norm * (hat_ro * hat_v + hat_v * hat_ro) + ((2 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2)) / 2 / norm / norm / norm / norm - 1 / 8 / norm / norm / std::sin(norm / 2) / std::sin(norm / 2)) * hat_v * (hat_ro * hat_v + hat_v * hat_ro) * hat_v) * jac_v;
}
else
{
res.block<3,3>(i*3, 0) = -0.5 * jac_v * hat_ro * jac_v;
}
}
// return res;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const SEN<scalar, DOF, dim_of_mat, Options>& q){
for(int i=0; i<dim_of_mat; i++)
{
for(int j = 0; j < dim_of_mat; j++)
{
os << q.mat(i, j) << " ";
}
}
return os;
}
friend std::istream& operator>>(std::istream &is, SEN<scalar, DOF, dim_of_mat, Options>& q){
// vect<dim_of_mat * dim_of_mat,scalar> coeffs;
for(int i=0; i<dim_of_mat; i++)
{
for(int j = 0; j < dim_of_mat; j++)
{
is >> q.mat(i, j);
}
}
// is >> q.mat;
// coeffs;
// q.coeffs() = coeffs.normalized();
return is;
}
//! @name Helper functions
//{
/**
* Calculate the exponential map. In matrix terms this would correspond
* to the Rodrigues formula.
*/
// FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround
// static SO3 exp(MTK::vectview<const scalar, 3> dvec, scalar scale = 1){
static SEN exp(const Eigen::Matrix<scalar, DOF, 1>& dvec, scalar scale = 1){
SEN res;
res.mat = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Identity();
Eigen::Matrix<scalar, 3, 3> exp_; //, jac;
Eigen::MatrixXd jac;
Eigen::Matrix<scalar, 3, 1> psi;
Eigen::VectorXd minus_psi;
psi = dvec.template block<3,1>(0, 0);
minus_psi = -psi;
SO3_type SO3_temp;
exp_ = SO3_type::exp(psi);
SO3_temp.Jacob_right(minus_psi, jac);
res.mat.template block<3,3>(0, 0) = exp_;
for(int i = 3; i < DOF / 3 + 2; i++)
{
res.mat.template block<3, 1>(0, i) = jac * dvec.template block<3,1>(i + (i-3)*3,0);
}
return res;
}
/**
* Calculate the inverse of @c exp.
* Only guarantees that <code>exp(log(x)) == x </code>
*/
static Eigen::Matrix<scalar, DOF, 1> log(base &orient){
Eigen::Matrix<scalar, DOF, 1> res;
Eigen::Matrix<scalar, 3, 1> psi;
Eigen::VectorXd minus_psi;
Eigen::Matrix<scalar, 3, 3> mat_psi;
Eigen::MatrixXd jac;
mat_psi = orient.template block<3, 3>(0, 0);
SO3_type SO3_temp;
SO3_type exp_psi(mat_psi);
psi = SO3_type::log(exp_psi);
minus_psi = -psi;
SO3_temp.Jacob_right_inv(minus_psi, jac);
for(int i = 3; i < dim_of_mat; i++)
{
res.template block<3,1>(i + (i-3)*3,0) = jac * orient.template block<3, 1>(0, i);
}
return res;
}
};
} // namespace MTK
#endif /*SON_H_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/SOn.hpp
* @brief Standard Orthogonal Groups i.e.\ rotatation groups.
*/
#ifndef SON_H_
#define SON_H_
#include <Eigen/Geometry>
#include "vect.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Two-dimensional orientations represented as scalar.
* There is no guarantee that the representing scalar is within any interval,
* but the result of boxminus will always have magnitude @f$\le\pi @f$.
*/
template<class _scalar = double, int Options = Eigen::AutoAlign>
struct SO2 : public Eigen::Rotation2D<_scalar> {
enum {DOF = 1, DIM = 2, TYP = 3};
typedef _scalar scalar;
typedef Eigen::Rotation2D<scalar> base;
typedef vect<DIM, scalar, Options> vect_type;
//! Construct from angle
SO2(const scalar& angle = 0) : base(angle) { }
//! Construct from Eigen::Rotation2D
SO2(const base& src) : base(src) {}
/**
* Construct from 2D vector.
* Resulting orientation will rotate the first unit vector to point to vec.
*/
SO2(const vect_type &vec) : base(atan2(vec[1], vec[0])) {};
//! Calculate @c this->inverse() * @c r
SO2 operator%(const base &r) const {
return base::inverse() * r;
}
//! Calculate @c this->inverse() * @c r
template<class Derived>
vect_type operator%(const Eigen::MatrixBase<Derived> &vec) const {
return base::inverse() * vec;
}
//! Calculate @c *this * @c r.inverse()
SO2 operator/(const SO2 &r) const {
return *this * r.inverse();
}
//! Gets the angle as scalar.
operator scalar() const {
return base::angle();
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
//! @name Manifold requirements
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
base::angle() += scale * vec[0];
}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
base::angle() += scale * vec[0];
}
void boxminus(MTK::vectview<scalar, DOF> res, const SO2<scalar>& other) const {
res[0] = MTK::normalize(base::angle() - other.angle(), scalar(MTK::pi));
}
friend std::istream& operator>>(std::istream &is, SO2<scalar>& ang){
return is >> ang.angle();
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
};
/**
* Three-dimensional orientations represented as Quaternion.
* It is assumed that the internal Quaternion always stays normalized,
* should this not be the case, call inherited member function @c normalize().
*/
template<class _scalar = double> //, int Options = Eigen::AutoAlign>
struct SO3 : public Eigen::Matrix<_scalar, 3, 3> {
enum {DOF = 3, DIM = 3, TYP = 2};
typedef _scalar scalar;
typedef Eigen::Matrix<scalar, 3, 3> base;
typedef Eigen::Matrix<scalar, 3, 3> Matrix;
typedef vect<DIM, scalar> vect_type;
/**
* Construct from Eigen::Quaternion.
* @note Non-normalized input may result result in spurious behavior.
*/
SO3(const base& src = base::Identity()) : base(src) {}
/**
* Construct from rotation matrix.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
template<class Derived>
SO3(const Eigen::MatrixBase<Derived>& matrix) : base(matrix) {}
/**
* Construct from arbitrary rotation type.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
// template<class Derived>
// SO3(const Eigen::RotationBase<Derived, 3>& rotation) : base(rotation.derived()) {}
//! @name Manifold requirements
// SO3 operator=(const base &r) const {
// return r;
// }
// //! Calculate @c *this * @c r.inverse()
// SO3 operator*(const SO3 &r) const {
// return *this * r;
// }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SO3 delta = exp(vec, scale);
*this = *this * delta;
}
void boxminus(MTK::vectview<scalar, DOF> res, const SO3<scalar>& other) const {
res = SO3::log(other.transpose() * *this);
}
//}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SO3 delta = exp(vec, scale);
*this = *this * delta;
}
// void hat(MTK::vectview<const scalar, DOF>& v, Eigen::Matrix<scalar, 3, 3> &res) {
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
// Eigen::Matrix<scalar, 3, 3> res;
res << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
// return res;
}
// void Jacob_right_inv(MTK::vectview<const scalar, DOF> vec, Eigen::Matrix<scalar, 3, 3> & res){
void Jacob_right_inv(Eigen::VectorXd& vec, Eigen::MatrixXd &res){
Eigen::MatrixXd hat_v;
hat(vec, hat_v);
if(vec.norm() > MTK::tolerance<scalar>())
{
res = Eigen::Matrix<scalar, 3, 3>::Identity() + 0.5 * hat_v + (1 - vec.norm() * std::cos(vec.norm() / 2) / 2 / std::sin(vec.norm() / 2)) * hat_v * hat_v / vec.squaredNorm();
}
else
{
res = Eigen::Matrix<scalar, 3, 3>::Identity();
}
// return res;
}
// void Jacob_right(MTK::vectview<const scalar, DOF> & v, Eigen::Matrix<scalar, 3, 3> &res){
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res){
Eigen::MatrixXd hat_v;
hat(v, hat_v);
double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
double norm = std::sqrt(squaredNorm);
if(norm < MTK::tolerance<scalar>()){
res = Eigen::Matrix<scalar, 3, 3>::Identity();
}
else{
res = Eigen::Matrix<scalar, 3, 3>::Identity() - (1 - std::cos(norm)) / squaredNorm * hat_v + (1 - std::sin(norm) / norm) / squaredNorm * hat_v * hat_v;
}
// return res;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const SO3<scalar>& q){ // wrong!
return os << q.data() << " ";
}
friend std::istream& operator>>(std::istream &is, SO3<scalar>& q){ // wrong!
SO3<scalar> coeffs;
is >> coeffs;
q = coeffs;
return is;
}
//! @name Helper functions
//{
/**
* Calculate the exponential map. In matrix terms this would correspond
* to the Rodrigues formula.
*/
// FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround
// static SO3 exp(MTK::vectview<const scalar, 3> dvec, scalar scale = 1){
static SO3 exp(const Eigen::Matrix<scalar, 3, 1>& dvec, scalar scale = 1){
Eigen::Matrix<scalar, 3, 1> ang = dvec * scale;
double ang_norm = ang.norm();
Eigen::Matrix<double, 3, 3> Eye3 = Eigen::Matrix<double, 3, 3>::Identity();
if (ang_norm > 0.0000001)
{
Eigen::Matrix<double, 3, 1> r_axis = ang / ang_norm;
Eigen::Matrix<double, 3, 3> K;
K << 0.0, -r_axis(2), r_axis(1),
r_axis(2), 0.0, -r_axis(0),
-r_axis(1), r_axis(0), 0.0; // SKEW_SYM_MATRX(r_axis);
/// Roderigous Tranformation
return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K;
}
else
{
return Eye3;
}
}
/**
* Calculate the inverse of @c exp.
* Only guarantees that <code>exp(log(x)) == x </code>
*/
static Eigen::Vector3d log(const SO3 &orient){
double theta = (orient.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (orient.trace() - 1));
Eigen::Matrix<double,3,1> K(orient(2,1) - orient(1,2), orient(0,2) - orient(2,0), orient(1,0) - orient(0,1));
return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K);
}
};
namespace internal {
template<class Scalar, int Options>
struct UnalignedType<SO2<Scalar, Options > >{
typedef SO2<Scalar, Options | Eigen::DontAlign> type;
};
template<class Scalar>
struct UnalignedType<SO3<Scalar> >{
typedef SO3<Scalar> type;
};
} // namespace internal
} // namespace MTK
#endif /*SON_H_*/

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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/vect.hpp
* @brief Basic vectors interpreted as manifolds.
*
* This file also implements a simple wrapper for matrices, for arbitrary scalars
* and for positive scalars.
*/
#ifndef VECT_H_
#define VECT_H_
#include <iosfwd>
#include <iostream>
#include <vector>
#include "../src/vectview.hpp"
namespace MTK {
static const Eigen::IOFormat IO_no_spaces(Eigen::StreamPrecision, Eigen::DontAlignCols, ",", ",", "", "", "[", "]");
/**
* A simple vector class.
* Implementation is basically a wrapper around Eigen::Matrix with manifold
* requirements added.
*/
template<int D = 3, class _scalar = double, int _Options=Eigen::AutoAlign>
struct vect : public Eigen::Matrix<_scalar, D, 1, _Options> {
typedef Eigen::Matrix<_scalar, D, 1, _Options> base;
enum {DOF = D, DIM = D, TYP = 0};
typedef _scalar scalar;
//using base::operator=;
/** Standard constructor. Sets all values to zero. */
vect(const base &src = base::Zero()) : base(src) {}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE vect(const Eigen::DenseBase<OtherDerived>& other) : base(other) {}
/** Construct from memory. */
vect(const scalar* src, int size = DOF) : base(base::Map(src, size)) { }
void boxplus(MTK::vectview<const scalar, D> vec, scalar scale=1) {
*this += scale * vec;
}
void boxminus(MTK::vectview<scalar, D> res, const vect<D, scalar>& other) const {
res = *this - other;
}
void oplus(MTK::vectview<const scalar, D> vec, scalar scale=1) {
*this += scale * vec;
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const vect<D, scalar, _Options>& v){
// Eigen sometimes messes with the streams flags, so output manually:
for(int i=0; i<DOF; ++i)
os << v(i) << " ";
return os;
}
friend std::istream& operator>>(std::istream &is, vect<D, scalar, _Options>& v){
char term=0;
is >> std::ws; // skip whitespace
switch(is.peek()) {
case '(': term=')'; is.ignore(1); break;
case '[': term=']'; is.ignore(1); break;
case '{': term='}'; is.ignore(1); break;
default: break;
}
if(D==Eigen::Dynamic) {
assert(term !=0 && "Dynamic vectors must be embraced");
std::vector<scalar> temp;
while(is.good() && is.peek() != term) {
scalar x;
is >> x;
temp.push_back(x);
if(is.peek()==',') is.ignore(1);
}
v = vect::Map(temp.data(), temp.size());
} else
for(int i=0; i<v.size(); ++i){
is >> v[i];
if(is.peek()==',') { // ignore commas between values
is.ignore(1);
}
}
if(term!=0) {
char x;
is >> x;
if(x!=term) {
is.setstate(is.badbit);
// assert(x==term && "start and end bracket do not match!");
}
}
return is;
}
template<int dim>
vectview<scalar, dim> tail(){
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template tail<dim>();
}
template<int dim>
vectview<const scalar, dim> tail() const{
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template tail<dim>();
}
template<int dim>
vectview<scalar, dim> head(){
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template head<dim>();
}
template<int dim>
vectview<const scalar, dim> head() const{
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template head<dim>();
}
};
/**
* A simple matrix class.
* Implementation is basically a wrapper around Eigen::Matrix with manifold
* requirements added, i.e., matrix is viewed as a plain vector for that.
*/
template<int M, int N, class _scalar = double, int _Options = Eigen::Matrix<_scalar, M, N>::Options>
struct matrix : public Eigen::Matrix<_scalar, M, N, _Options> {
typedef Eigen::Matrix<_scalar, M, N, _Options> base;
enum {DOF = M * N, TYP = 4, DIM=0};
typedef _scalar scalar;
using base::operator=;
/** Standard constructor. Sets all values to zero. */
matrix() {
base::setZero();
}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE matrix(const Eigen::MatrixBase<OtherDerived>& other) : base(other) {}
/** Construct from memory. */
matrix(const scalar* src) : base(src) { }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
*this += scale * base::Map(vec.data());
}
void boxminus(MTK::vectview<scalar, DOF> res, const matrix& other) const {
base::Map(res.data()) = *this - other;
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
*this += scale * base::Map(vec.data());
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const matrix<M, N, scalar, _Options>& mat){
for(int i=0; i<DOF; ++i){
os << mat.data()[i] << " ";
}
return os;
}
friend std::istream& operator>>(std::istream &is, matrix<M, N, scalar, _Options>& mat){
for(int i=0; i<DOF; ++i){
is >> mat.data()[i];
}
return is;
}
};// @todo What if M / N = Eigen::Dynamic?
/**
* A simple scalar type.
*/
template<class _scalar = double>
struct Scalar {
enum {DOF = 1, TYP = 5, DIM=0};
typedef _scalar scalar;
scalar value;
Scalar(const scalar& value = scalar(0)) : value(value) {}
operator const scalar&() const { return value; }
operator scalar&() { return value; }
Scalar& operator=(const scalar& val) { value = val; return *this; }
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
value += scale * vec[0];
}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
value += scale * vec[0];
}
void boxminus(MTK::vectview<scalar, DOF> res, const Scalar& other) const {
res[0] = *this - other;
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
};
/**
* Positive scalars.
* Boxplus is implemented using multiplication by @f$x\boxplus\delta = x\cdot\exp(\delta) @f$.
*/
template<class _scalar = double>
struct PositiveScalar {
enum {DOF = 1, TYP = 6, DIM=0};
typedef _scalar scalar;
scalar value;
PositiveScalar(const scalar& value = scalar(1)) : value(value) {
assert(value > scalar(0));
}
operator const scalar&() const { return value; }
PositiveScalar& operator=(const scalar& val) { assert(val>0); value = val; return *this; }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
value *= std::exp(scale * vec[0]);
}
void boxminus(MTK::vectview<scalar, DOF> res, const PositiveScalar& other) const {
res[0] = std::log(*this / other);
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
value *= std::exp(scale * vec[0]);
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::istream& operator>>(std::istream &is, PositiveScalar<scalar>& s){
is >> s.value;
assert(s.value > 0);
return is;
}
};
template<class _scalar = double>
struct Complex : public std::complex<_scalar>{
enum {DOF = 2, TYP = 7, DIM=0};
typedef _scalar scalar;
typedef std::complex<scalar> Base;
Complex(const Base& value) : Base(value) {}
Complex(const scalar& re = 0.0, const scalar& im = 0.0) : Base(re, im) {}
Complex(const MTK::vectview<const scalar, 2> &in) : Base(in[0], in[1]) {}
template<class Derived>
Complex(const Eigen::DenseBase<Derived> &in) : Base(in[0], in[1]) {}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
Base::real() += scale * vec[0];
Base::imag() += scale * vec[1];
};
void boxminus(MTK::vectview<scalar, DOF> res, const Complex& other) const {
Complex diff = *this - other;
res << diff.real(), diff.imag();
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
Base::real() += scale * vec[0];
Base::imag() += scale * vec[1];
};
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
scalar squaredNorm() const {
return std::pow(Base::real(),2) + std::pow(Base::imag(),2);
}
const scalar& operator()(int i) const {
assert(0<=i && i<2 && "Index out of range");
return i==0 ? Base::real() : Base::imag();
}
scalar& operator()(int i){
assert(0<=i && i<2 && "Index out of range");
return i==0 ? Base::real() : Base::imag();
}
};
namespace internal {
template<int dim, class Scalar, int Options>
struct UnalignedType<vect<dim, Scalar, Options > >{
typedef vect<dim, Scalar, Options | Eigen::DontAlign> type;
};
} // namespace internal
} // namespace MTK
#endif /*VECT_H_*/

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/*
* Copyright (c) 2010--2011, Universitaet Bremen and DFKI GmbH
* All rights reserved.
*
* Author: Rene Wagner <rene.wagner@dfki.de>
* Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the DFKI GmbH
* nor the names of its contributors may be used to endorse or
* promote products derived from this software without specific
* prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef WRAPPED_CV_MAT_HPP_
#define WRAPPED_CV_MAT_HPP_
#include <Eigen/Core>
#include <opencv/cv.h>
namespace MTK {
template<class f_type>
struct cv_f_type;
template<>
struct cv_f_type<double>
{
enum {value = CV_64F};
};
template<>
struct cv_f_type<float>
{
enum {value = CV_32F};
};
/**
* cv_mat wraps a CvMat around an Eigen Matrix
*/
template<int rows, int cols, class f_type = double>
class cv_mat : public matrix<rows, cols, f_type, cols==1 ? Eigen::ColMajor : Eigen::RowMajor>
{
typedef matrix<rows, cols, f_type, cols==1 ? Eigen::ColMajor : Eigen::RowMajor> base_type;
enum {type_ = cv_f_type<f_type>::value};
CvMat cv_mat_;
public:
cv_mat()
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
cv_mat(const cv_mat& oth) : base_type(oth)
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
template<class Derived>
cv_mat(const Eigen::MatrixBase<Derived> &value) : base_type(value)
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
template<class Derived>
cv_mat& operator=(const Eigen::MatrixBase<Derived> &value)
{
base_type::operator=(value);
return *this;
}
cv_mat& operator=(const cv_mat& value)
{
base_type::operator=(value);
return *this;
}
// FIXME: Maybe overloading operator& is not a good idea ...
CvMat* operator&()
{
return &cv_mat_;
}
const CvMat* operator&() const
{
return &cv_mat_;
}
};
} // namespace MTK
#endif /* WRAPPED_CV_MAT_HPP_ */

339
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GNU GENERAL PUBLIC LICENSE
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Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
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Everyone is permitted to copy and distribute verbatim copies
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Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.

489
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## IKFoM
**IKFoM** (Iterated Kalman Filters on Manifolds) is a computationally efficient and convenient toolkit for deploying iterated Kalman filters on various robotic systems, especially systems operating on high-dimension manifold. It implements a manifold-embedding Kalman filter which separates the manifold structures from system descriptions and is able to be used by only defining the system in a canonical form and calling the respective steps accordingly. The current implementation supports the full iterated Kalman filtering for systems on manifold <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbb{R}^m\times&space;SO(3)\times\cdots\times&space;SO(3)\times\mathbb{S}^2\times\cdots\times\mathbb{S}^2" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbb{R}^m\times&space;SO(3)\times\cdots\times&space;SO(3)\times\mathbb{S}^2\times\cdots\times\mathbb{S}^2" title="\mathbb{R}^m\times SO(3)\times\cdots\times SO(3)\times\mathbb{S}^2\times\cdots\times\mathbb{S}^2" /></a> and any of its sub-manifolds, and it is extendable to other types of manifold when necessary.
**Developers**
[Dongjiao He](https://github.com/Joanna-HE)
**Our related video**: https://youtu.be/sz_ZlDkl6fA
## 1. Prerequisites
### 1.1. **Eigen && Boost**
Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
Boost >= 1.65.
## 2. Usage when the measurement is of constant dimension and type.
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
1. include the necessary head file:
```
#include<esekfom/esekfom.hpp>
```
2. Select and instantiate the primitive manifolds:
```
typedef MTK::SO3<double> SO3; // scalar type of variable: double
typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3
typedef MTK::S2<double, 98, 10, 1> S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1
```
3. Build system state, input and measurement as compound manifolds which are composed of the primitive manifolds:
```
MTK_BUILD_MANIFOLD(state, // name of compound manifold: state
((vect3, pos)) // ((primitive manifold type, name of variable))
((vect3, vel))
((SO3, rot))
((vect3, bg))
((vect3, ba))
((S2, grav))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
);
```
4. Implement the vector field <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" title="\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)" /></a> that is defined as <a href="https://latex.codecogs.com/svg.image?\mathbf{x}_{k&plus;1}&space;=&space;\mathbf{x}_k\oplus\Delta&space;t\mathbf{f}(\mathbf{x}_k,&space;\mathbf{u}_k,&space;\mathbf{w}_k);\hat{\mathbf{x}}_{k&plus;1}&space;=&space;\hat{\mathbf{x}}_k\oplus\Delta&space;t\mathbf{f}(\hat{\mathbf{x}}_k,&space;\mathbf{u}_k,&space;\mathbf{0})"><see here>, and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{u}, \mathbf{0}\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)}{\partial\mathbf{w}}" /></a>, where w=0 could be left out:
```
Eigen::Matrix<double, state_length, 1> f(state &s, const input &i) {
Eigen::Matrix<double, state_length, 1> res = Eigen::Matrix<double, state_length, 1>::Zero();
res(0) = s.vel[0];
res(1) = s.vel[1];
res(2) = s.vel[2];
return res;
}
Eigen::Matrix<double, state_length, state_dof> df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 {
Eigen::Matrix<double, state_length, state_dof> cov = Eigen::Matrix<double, state_length, state_dof>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, state_length, process_noise_dof> df_dw(state &s, const input &i) {
Eigen::Matrix<double, state_length, process_noise_dof> cov = Eigen::Matrix<double, state_length, process_noise_dof>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
return cov;
}
```
Those functions would be called during the ekf state predict
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
measurement h(state &s, bool &valid) // the iteration stops before convergence whenever the user set valid as false
{
if (condition){ valid = false;
} // other conditions could be used to stop the ekf update iteration before convergence, otherwise the iteration will not stop until the condition of convergence is satisfied.
measurement h_;
h_.position = s.pos;
return h_;
}
Eigen::Matrix<double, measurement_dof, state_dof> dh_dx(state &s) {}
Eigen::Matrix<double, measurement_dof, measurement_noise_dof> dh_dv(state &s) {}
```
Those functions would be called during the ekf state update
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof>::cov init_P;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf;
```
where **process_noise_dof** is the dimension of process noise, with the type of std int, and so for **measurement_noise_dof**.
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init(f, df_dx, df_dw, h, dh_dx, dh_dv, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated(z, R); // measurement noise covariance: R, an Eigen matrix
```
*Remarks(1):*
- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows.
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
measurement h_share(state &s, esekfom::share_datastruct<state, measurement, measurement_noise_dof> &share_data)
{
if(share_data.converge) {} // this value is true means iteration is converged
if(condition) share_data.valid = false; // the iteration stops before convergence when this value is false if other conditions are satified
share_data.h_x = H_x; // H_x is the result matrix of the first differentiation
share_data.h_v = H_v; // H_v is the result matrix of the second differentiation
share_data.R = R; // R is the measurement noise covariance
share_data.z = z; // z is the obtained measurement
measurement h_;
h_.position = s.pos;
return h_;
}
```
This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof>::cov init_P;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf;
```
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_share(f, df_dx, df_dw, h_share, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated_share();
```
*Remarks(2):*
- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**:
```
state set_x;
kf.change_x(set_x);
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof>::cov set_P;
kf.change_P(set_P);
```
## 3. Usage when the measurement is an Eigen vector of changing dimension.
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
1. include the necessary head file:
```
#include<esekfom/esekfom.hpp>
```
2. Select and instantiate the primitive manifolds:
```
typedef MTK::SO3<double> SO3; // scalar type of variable: double
typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3
typedef MTK::S2<double, 98, 10, 1> S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1
```
3. Build system state and input as compound manifolds which are composed of the primitive manifolds:
```
MTK_BUILD_MANIFOLD(state, // name of compound manifold: state
((vect3, pos)) // ((primitive manifold type, name of variable))
((vect3, vel))
((SO3, rot))
((vect3, bg))
((vect3, ba))
((S2, grav))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
);
```
4. Implement the vector field <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" title="\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)" /></a> that is defined as <a href="https://latex.codecogs.com/svg.image?\mathbf{x}_{k&plus;1}&space;=&space;\mathbf{x}_k\oplus\Delta&space;t\mathbf{f}(\mathbf{x}_k,&space;\mathbf{u}_k,&space;\mathbf{w}_k);\hat{\mathbf{x}}_{k&plus;1}&space;=&space;\hat{\mathbf{x}}_k\oplus\Delta&space;t\mathbf{f}(\hat{\mathbf{x}}_k,&space;\mathbf{u}_k,&space;\mathbf{0})"> <see here>, and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{u}, \mathbf{0}\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)}{\partial\mathbf{w}}" /></a>, where w=0 could be left out:
```
Eigen::Matrix<double, state_length, 1> f(state &s, const input &i) {
Eigen::Matrix<double, state_length, 1> res = Eigen::Matrix<double, state_length, 1>::Zero();
res(0) = s.vel[0];
res(1) = s.vel[1];
res(2) = s.vel[2];
return res;
}
Eigen::Matrix<double, state_length, state_dof> df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 {
Eigen::Matrix<double, state_length, state_dof> cov = Eigen::Matrix<double, state_length, state_dof>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, state_length, process_noise_dof> df_dw(state &s, const input &i) {
Eigen::Matrix<double, state_length, process_noise_dof> cov = Eigen::Matrix<double, state_length, process_noise_dof>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
return cov;
}
```
Those functions would be called during ekf state predict
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
Eigen::Matrix<double, Eigen::Dynamic, 1> h(state &s, bool &valid) //the iteration stops before convergence when valid is false {
if (condition){ valid = false;
} // other conditions could be used to stop the ekf update iteration before convergence, otherwise the iteration will not stop until the condition of convergence is satisfied.
Eigen::Matrix<double, Eigen::Dynamic, 1> h_;
h_(0) = s.pos[0];
return h_;
}
Eigen::Matrix<double, Eigen::Dynamic, state_dof> dh_dx(state &s) {}
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> dh_dv(state &s) {}
```
Those functions would be called during ekf state update
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
where **process_noise_dof** is the dimension of process noise, with the type of std int, and so for **measurement_noise_dof**
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn(f, df_dx, df_dw, h, dh_dx, dh_dv, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated_dyn(z, R); // measurement noise covariance: R, an Eigen matrix
```
*Remarks(1):*
- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows.
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
Eigen::Matrix<double, Eigen::Dynamic, 1> h_dyn_share(state &s, esekfom::dyn_share_datastruct<double> &dyn_share_data)
{
if(dyn_share_data.converge) {} // this value is true means iteration is converged
if(condition) share_data.valid = false; // the iteration stops before convergence when this value is false if other conditions are satified
dyn_share_data.h_x = H_x; // H_x is the result matrix of the first differentiation
dyn_share_data.h_v = H_v; // H_v is the result matrix of the second differentiation
dyn_share_data.R = R; // R is the measurement noise covariance
dyn_share_data.z = z; // z is the obtained measurement
Eigen::Matrix<double, Eigen::Dynamic, 1> h_;
h_(0) = s.pos[0];
return h_;
}
This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function
```
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn_share(f, df_dx, df_dw, h_dyn_share, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated_dyn_share();
```
*Remarks(2):*
- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**:
```
state set_x;
kf.change_x(set_x);
esekfom::esekf<state, process_noise_dof, input>::cov set_P;
kf.change_P(set_P);
```
## 4. Usage when the measurement is a changing manifold during the run time.
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
1. include the necessary head file:
```
#include<esekfom/esekfom.hpp>
```
2. Select and instantiate the primitive manifolds:
```
typedef MTK::SO3<double> SO3; // scalar type of variable: double
typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3
typedef MTK::S2<double, 98, 10, 1> S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1
```
3. Build system state and input as compound manifolds which are composed of the primitive manifolds:
```
MTK_BUILD_MANIFOLD(state, // name of compound manifold: state
((vect3, pos)) // ((primitive manifold type, name of variable))
((vect3, vel))
((SO3, rot))
((vect3, bg))
((vect3, ba))
((S2, grav))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
);
```
4. Implement the vector field <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" title="\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)" /></a> that is defined as <a href="https://latex.codecogs.com/svg.image?\mathbf{x}_{k&plus;1}&space;=&space;\mathbf{x}_k\oplus\Delta&space;t\mathbf{f}(\mathbf{x}_k,&space;\mathbf{u}_k,&space;\mathbf{w}_k);\hat{\mathbf{x}}_{k&plus;1}&space;=&space;\hat{\mathbf{x}}_k\oplus\Delta&space;t\mathbf{f}(\hat{\mathbf{x}}_k,&space;\mathbf{u}_k,&space;\mathbf{0})"> <see here>, and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{u}, \mathbf{0}\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)}{\partial\mathbf{w}}" /></a>, where w=0 could be left out:
```
Eigen::Matrix<double, state_length, 1> f(state &s, const input &i) {
Eigen::Matrix<double, state_length, 1> res = Eigen::Matrix<double, state_length, 1>::Zero();
res(0) = s.vel[0];
res(1) = s.vel[1];
res(2) = s.vel[2];
return res;
}
Eigen::Matrix<double, state_length, state_dof> df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 {
Eigen::Matrix<double, state_length, state_dof> cov = Eigen::Matrix<double, state_length, state_dof>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, state_length, process_noise_dof> df_dw(state &s, const input &i) {
Eigen::Matrix<double, state_length, process_noise_dof> cov = Eigen::Matrix<double, state_length, process_noise_dof>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
return cov;
}
```
Those functions would be called during ekf state predict
5. Implement the differentiation of the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
Eigen::Matrix<double, Eigen::Dynamic, state_dof> dh_dx(state &s, bool &valid) {} //the iteration stops before convergence when valid is false
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> dh_dv(state &s, bool &valid) {}
```
Those functions would be called during ekf state update
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2)
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
Where **process_noise_dof** is the dimension of process noise, of type of std int
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn_runtime(f, df_dx, df_dw, dh_dx, dh_dv, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q
```
9. Once a measurement **z** is received, build system measurement as compound manifolds following step 3 and implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> :
```
measurement h(state &s, bool &valid) //the iteration stops before convergence when valid is false
{
if (condition) valid = false; // the update iteration could be stopped when the condition other than convergence is satisfied
measurement h_;
h_.pos = s.pos;
return h_;
}
```
then an iterated update is executed:
```
kf.update_iterated_dyn_runtime(z, R, h); // measurement noise covariance: R, an Eigen matrix
```
*Remarks(1):*
- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows.
5. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
6. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn_runtime_share(f, df_dx, df_dw, Maximum_iter, epsi);
```
7. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q. an Eigen matrix
```
8. Once a measurement **z** is received, build system measurement as compound manifolds following step 3 and implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
measurement h_dyn_runtime_share(state &s, esekfom::dyn_runtime_share_datastruct<double> &dyn_runtime_share_data)
{
if(dyn_runtime_share_data.converge) {} // this value is true means iteration is converged
if(condition) dyn_runtime_share_data.valid = false; // the iteration stops before convergence when this value is false, if conditions other than convergence is satisfied
dyn_runtime_share_data.h_x = H_x; // H_x is the result matrix of the first differentiation
dyn_runtime_share_data.h_v = H_v; // H_v is the result matrix of the second differentiation
dyn_runtime_share_data.R = R; // R is the measurement noise covariance
measurement h_;
h_.pos = s.pos;
return h_;
}
```
This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function
then an iterated update is executed:
```
kf.update_iterated_dyn_runtime_share(z, h_dyn_runtime_share);
```
*Remarks(2):*
- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**:
```
state set_x;
kf.change_x(set_x);
esekfom::esekf<state, process_noise_dof, input>::cov set_P;
kf.change_P(set_P);
```
## 5. Run the sample
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
In the **Samples** file folder, there is the scource code that applys the **IKFoM** on the original source code from [FAST LIO](https://github.com/hku-mars/FAST_LIO). Please follow the README.md shown in that repository excepting the step **2. Build**, which is modified as:
```
cd ~/catkin_ws/src
cp -r ~/IKFoM/Samples/FAST_LIO-stable FAST_LIO-stable
cd ..
catkin_make
source devel/setup.bash
```
## 6.Acknowledgments
Thanks for C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.

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#ifndef COMMON_LIB_H
#define COMMON_LIB_H
#include <so3_math.h>
#include <Eigen/Eigen>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <sensor_msgs/Imu.h>
#include <nav_msgs/Odometry.h>
#include <tf/transform_broadcaster.h>
#include <eigen_conversions/eigen_msg.h>
using namespace std;
using namespace Eigen;
#define PI_M (3.14159265358)
#define G_m_s2 (9.81) // Gravaty const in GuangDong/China
#define DIM_STATE (18) // Dimension of states (Let Dim(SO(3)) = 3)
#define DIM_PROC_N (12) // Dimension of process noise (Let Dim(SO(3)) = 3)
#define CUBE_LEN (6.0)
#define LIDAR_SP_LEN (2)
#define INIT_COV (0.0001)
#define NUM_MATCH_POINTS (5)
#define MAX_MEAS_DIM (10000)
#define VEC_FROM_ARRAY(v) v[0],v[1],v[2]
#define VEC_FROM_ARRAY_SIX(v) v[0],v[1],v[2],v[3],v[4],v[5]
#define MAT_FROM_ARRAY(v) v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7],v[8]
#define CONSTRAIN(v,min,max) ((v>min)?((v<max)?v:max):min)
#define ARRAY_FROM_EIGEN(mat) mat.data(), mat.data() + mat.rows() * mat.cols()
#define STD_VEC_FROM_EIGEN(mat) vector<decltype(mat)::Scalar> (mat.data(), mat.data() + mat.rows() * mat.cols())
#define DEBUG_FILE_DIR(name) (string(string(ROOT_DIR) + "Log/"+ name))
typedef pcl::PointXYZINormal PointType;
typedef pcl::PointXYZRGB PointTypeRGB;
typedef pcl::PointCloud<PointType> PointCloudXYZI;
typedef pcl::PointCloud<PointTypeRGB> PointCloudXYZRGB;
typedef vector<PointType, Eigen::aligned_allocator<PointType>> PointVector;
typedef Vector3d V3D;
typedef Matrix3d M3D;
typedef Vector3f V3F;
typedef Matrix3f M3F;
#define MD(a,b) Matrix<double, (a), (b)>
#define VD(a) Matrix<double, (a), 1>
#define MF(a,b) Matrix<float, (a), (b)>
#define VF(a) Matrix<float, (a), 1>
const M3D Eye3d(M3D::Identity());
const M3F Eye3f(M3F::Identity());
const V3D Zero3d(0, 0, 0);
const V3F Zero3f(0, 0, 0);
struct MeasureGroup // Lidar data and imu dates for the curent process
{
MeasureGroup()
{
lidar_beg_time = 0.0;
lidar_last_time = 0.0;
this->lidar.reset(new PointCloudXYZI());
};
double lidar_beg_time;
double lidar_last_time;
PointCloudXYZI::Ptr lidar;
deque<sensor_msgs::Imu::ConstPtr> imu;
};
template <typename T>
T calc_dist(PointType p1, PointType p2){
T d = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) + (p1.z - p2.z) * (p1.z - p2.z);
return d;
}
template <typename T>
T calc_dist(Eigen::Vector3d p1, PointType p2){
T d = (p1(0) - p2.x) * (p1(0) - p2.x) + (p1(1) - p2.y) * (p1(1) - p2.y) + (p1(2) - p2.z) * (p1(2) - p2.z);
return d;
}
template<typename T>
std::vector<int> time_compressing(const PointCloudXYZI::Ptr &point_cloud)
{
int points_size = point_cloud->points.size();
int j = 0;
std::vector<int> time_seq;
// time_seq.clear();
time_seq.reserve(points_size);
for(int i = 0; i < points_size - 1; i++)
{
j++;
if (point_cloud->points[i+1].curvature > point_cloud->points[i].curvature)
{
time_seq.emplace_back(j);
j = 0;
}
}
if (j == 0)
{
time_seq.emplace_back(1);
}
else
{
time_seq.emplace_back(j+1);
}
return time_seq;
}
/* comment
plane equation: Ax + By + Cz + D = 0
convert to: A/D*x + B/D*y + C/D*z = -1
solve: A0*x0 = b0
where A0_i = [x_i, y_i, z_i], x0 = [A/D, B/D, C/D]^T, b0 = [-1, ..., -1]^T
normvec: normalized x0
*/
template<typename T>
bool esti_normvector(Matrix<T, 3, 1> &normvec, const PointVector &point, const T &threshold, const int &point_num)
{
MatrixXf A(point_num, 3);
MatrixXf b(point_num, 1);
b.setOnes();
b *= -1.0f;
for (int j = 0; j < point_num; j++)
{
A(j,0) = point[j].x;
A(j,1) = point[j].y;
A(j,2) = point[j].z;
}
normvec = A.colPivHouseholderQr().solve(b);
for (int j = 0; j < point_num; j++)
{
if (fabs(normvec(0) * point[j].x + normvec(1) * point[j].y + normvec(2) * point[j].z + 1.0f) > threshold)
{
return false;
}
}
normvec.normalize();
return true;
}
template<typename T>
bool esti_plane(Matrix<T, 4, 1> &pca_result, const PointVector &point, const T &threshold)
{
Matrix<T, NUM_MATCH_POINTS, 3> A;
Matrix<T, NUM_MATCH_POINTS, 1> b;
A.setZero();
b.setOnes();
b *= -1.0f;
for (int j = 0; j < NUM_MATCH_POINTS; j++)
{
A(j,0) = point[j].x;
A(j,1) = point[j].y;
A(j,2) = point[j].z;
}
Matrix<T, 3, 1> normvec = A.colPivHouseholderQr().solve(b);
T n = normvec.norm();
pca_result(0) = normvec(0) / n;
pca_result(1) = normvec(1) / n;
pca_result(2) = normvec(2) / n;
pca_result(3) = 1.0 / n;
for (int j = 0; j < NUM_MATCH_POINTS; j++)
{
if (fabs(pca_result(0) * point[j].x + pca_result(1) * point[j].y + pca_result(2) * point[j].z + pca_result(3)) > threshold)
{
return false;
}
}
return true;
}
#endif

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# ikd-Tree
ikd-Tree is an incremental k-d tree for robotic applications.

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src/Point-LIO/include/ikd-Tree/ikd_Tree.cpp Normal file
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src/Point-LIO/include/ikd-Tree/ikd_Tree.h Normal file
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#pragma once
#include <stdio.h>
#include <queue>
#include <pthread.h>
#include <chrono>
#include <time.h>
#include <unistd.h>
#include <math.h>
#include <algorithm>
#include <memory.h>
#include <pcl/point_types.h>
#define EPSS 1e-6
#define Minimal_Unbalanced_Tree_Size 10
#define Multi_Thread_Rebuild_Point_Num 1500
#define DOWNSAMPLE_SWITCH true
#define ForceRebuildPercentage 0.2
#define Q_LEN 1000000
using namespace std;
// typedef pcl::PointXYZINormal PointType;
// typedef vector<PointType, Eigen::aligned_allocator<PointType>> PointVector;
struct BoxPointType
{
float vertex_min[3];
float vertex_max[3];
};
enum operation_set
{
ADD_POINT,
DELETE_POINT,
DELETE_BOX,
ADD_BOX,
DOWNSAMPLE_DELETE,
PUSH_DOWN
};
enum delete_point_storage_set
{
NOT_RECORD,
DELETE_POINTS_REC,
MULTI_THREAD_REC
};
template <typename PointType>
class KD_TREE
{
// using MANUAL_Q_ = MANUAL_Q<typename PointType>;
// using PointVector = std::vector<PointType>;
// using MANUAL_Q_ = MANUAL_Q<typename PointType>;
public:
using PointVector = std::vector<PointType, Eigen::aligned_allocator<PointType>>;
using Ptr = std::shared_ptr<KD_TREE<PointType>>;
struct KD_TREE_NODE
{
PointType point;
int division_axis;
int TreeSize = 1;
int invalid_point_num = 0;
int down_del_num = 0;
bool point_deleted = false;
bool tree_deleted = false;
bool point_downsample_deleted = false;
bool tree_downsample_deleted = false;
bool need_push_down_to_left = false;
bool need_push_down_to_right = false;
bool working_flag = false;
pthread_mutex_t push_down_mutex_lock;
float node_range_x[2], node_range_y[2], node_range_z[2];
float radius_sq;
KD_TREE_NODE *left_son_ptr = nullptr;
KD_TREE_NODE *right_son_ptr = nullptr;
KD_TREE_NODE *father_ptr = nullptr;
// For paper data record
float alpha_del;
float alpha_bal;
};
struct Operation_Logger_Type
{
PointType point;
BoxPointType boxpoint;
bool tree_deleted, tree_downsample_deleted;
operation_set op;
};
// static const PointType zeroP;
struct PointType_CMP
{
PointType point;
float dist = 0.0;
PointType_CMP(PointType p = PointType(), float d = INFINITY)
{
this->point = p;
this->dist = d;
};
bool operator<(const PointType_CMP &a) const
{
if (fabs(dist - a.dist) < 1e-10)
return point.x < a.point.x;
else
return dist < a.dist;
}
};
class MANUAL_HEAP
{
public:
MANUAL_HEAP(int max_capacity = 100)
{
cap = max_capacity;
heap = new PointType_CMP[max_capacity];
heap_size = 0;
}
~MANUAL_HEAP()
{
delete[] heap;
}
void pop()
{
if (heap_size == 0)
return;
heap[0] = heap[heap_size - 1];
heap_size--;
MoveDown(0);
return;
}
PointType_CMP top()
{
return heap[0];
}
void push(PointType_CMP point)
{
if (heap_size >= cap)
return;
heap[heap_size] = point;
FloatUp(heap_size);
heap_size++;
return;
}
int size()
{
return heap_size;
}
void clear()
{
heap_size = 0;
return;
}
private:
PointType_CMP *heap;
void MoveDown(int heap_index)
{
int l = heap_index * 2 + 1;
PointType_CMP tmp = heap[heap_index];
while (l < heap_size)
{
if (l + 1 < heap_size && heap[l] < heap[l + 1])
l++;
if (tmp < heap[l])
{
heap[heap_index] = heap[l];
heap_index = l;
l = heap_index * 2 + 1;
}
else
break;
}
heap[heap_index] = tmp;
return;
}
void FloatUp(int heap_index)
{
int ancestor = (heap_index - 1) / 2;
PointType_CMP tmp = heap[heap_index];
while (heap_index > 0)
{
if (heap[ancestor] < tmp)
{
heap[heap_index] = heap[ancestor];
heap_index = ancestor;
ancestor = (heap_index - 1) / 2;
}
else
break;
}
heap[heap_index] = tmp;
return;
}
int heap_size = 0;
int cap = 0;
};
class MANUAL_Q
{
private:
int head = 0, tail = 0, counter = 0;
Operation_Logger_Type q[Q_LEN];
bool is_empty;
public:
void pop()
{
if (counter == 0)
return;
head++;
head %= Q_LEN;
counter--;
if (counter == 0)
is_empty = true;
return;
}
Operation_Logger_Type front()
{
return q[head];
}
Operation_Logger_Type back()
{
return q[tail];
}
void clear()
{
head = 0;
tail = 0;
counter = 0;
is_empty = true;
return;
}
void push(Operation_Logger_Type op)
{
q[tail] = op;
counter++;
if (is_empty)
is_empty = false;
tail++;
tail %= Q_LEN;
}
bool empty()
{
return is_empty;
}
int size()
{
return counter;
}
};
private:
// Multi-thread Tree Rebuild
bool termination_flag = false;
bool rebuild_flag = false;
pthread_t rebuild_thread;
pthread_mutex_t termination_flag_mutex_lock, rebuild_ptr_mutex_lock, working_flag_mutex, search_flag_mutex;
pthread_mutex_t rebuild_logger_mutex_lock, points_deleted_rebuild_mutex_lock;
// queue<Operation_Logger_Type> Rebuild_Logger;
MANUAL_Q Rebuild_Logger;
PointVector Rebuild_PCL_Storage;
KD_TREE_NODE **Rebuild_Ptr = nullptr;
int search_mutex_counter = 0;
static void *multi_thread_ptr(void *arg);
void multi_thread_rebuild();
void start_thread();
void stop_thread();
void run_operation(KD_TREE_NODE **root, Operation_Logger_Type operation);
// KD Tree Functions and augmented variables
int Treesize_tmp = 0, Validnum_tmp = 0;
float alpha_bal_tmp = 0.5, alpha_del_tmp = 0.0;
float delete_criterion_param = 0.5f;
float balance_criterion_param = 0.7f;
float downsample_size = 0.2f;
bool Delete_Storage_Disabled = false;
KD_TREE_NODE *STATIC_ROOT_NODE = nullptr;
PointVector Points_deleted;
PointVector Downsample_Storage;
PointVector Multithread_Points_deleted;
void InitTreeNode(KD_TREE_NODE *root);
void Test_Lock_States(KD_TREE_NODE *root);
void BuildTree(KD_TREE_NODE **root, int l, int r, PointVector &Storage);
void Rebuild(KD_TREE_NODE **root);
int Delete_by_range(KD_TREE_NODE **root, BoxPointType boxpoint, bool allow_rebuild, bool is_downsample);
void Delete_by_point(KD_TREE_NODE **root, PointType point, bool allow_rebuild);
void Add_by_point(KD_TREE_NODE **root, PointType point, bool allow_rebuild, int father_axis);
void Add_by_range(KD_TREE_NODE **root, BoxPointType boxpoint, bool allow_rebuild);
void Search(KD_TREE_NODE *root, int k_nearest, PointType point, MANUAL_HEAP &q, float max_dist); //priority_queue<PointType_CMP>
void Search_by_range(KD_TREE_NODE *root, BoxPointType boxpoint, PointVector &Storage);
void Search_by_radius(KD_TREE_NODE *root, PointType point, float radius, PointVector &Storage);
bool Criterion_Check(KD_TREE_NODE *root);
void Push_Down(KD_TREE_NODE *root);
void Update(KD_TREE_NODE *root);
void delete_tree_nodes(KD_TREE_NODE **root);
void downsample(KD_TREE_NODE **root);
bool same_point(PointType a, PointType b);
float calc_dist(PointType a, PointType b);
float calc_box_dist(KD_TREE_NODE *node, PointType point);
static bool point_cmp_x(PointType a, PointType b);
static bool point_cmp_y(PointType a, PointType b);
static bool point_cmp_z(PointType a, PointType b);
public:
KD_TREE(float delete_param = 0.5, float balance_param = 0.6, float box_length = 0.2);
~KD_TREE();
void Set_delete_criterion_param(float delete_param)
{
delete_criterion_param = delete_param;
}
void Set_balance_criterion_param(float balance_param)
{
balance_criterion_param = balance_param;
}
void set_downsample_param(float downsample_param)
{
downsample_size = downsample_param;
}
void InitializeKDTree(float delete_param = 0.5, float balance_param = 0.7, float box_length = 0.2);
int size();
int validnum();
void root_alpha(float &alpha_bal, float &alpha_del);
void Build(PointVector point_cloud);
void Nearest_Search(PointType point, int k_nearest, PointVector &Nearest_Points, vector<float> &Point_Distance, float max_dist = INFINITY);
void Box_Search(const BoxPointType &Box_of_Point, PointVector &Storage);
void Radius_Search(PointType point, const float radius, PointVector &Storage);
int Add_Points(PointVector &PointToAdd, bool downsample_on);
void Add_Point_Boxes(vector<BoxPointType> &BoxPoints);
void Delete_Points(PointVector &PointToDel);
int Delete_Point_Boxes(vector<BoxPointType> &BoxPoints);
void flatten(KD_TREE_NODE *root, PointVector &Storage, delete_point_storage_set storage_type);
void acquire_removed_points(PointVector &removed_points);
BoxPointType tree_range();
PointVector PCL_Storage;
KD_TREE_NODE *Root_Node = nullptr;
int max_queue_size = 0;
};
// template <typename PointType>
// PointType KD_TREE<PointType>::zeroP = PointType(0,0,0);

113
src/Point-LIO/include/so3_math.h Executable file
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#ifndef SO3_MATH_H
#define SO3_MATH_H
#include <math.h>
#include <Eigen/Core>
// #include <common_lib.h>
#define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0
template<typename T>
Eigen::Matrix<T, 3, 3> skew_sym_mat(const Eigen::Matrix<T, 3, 1> &v)
{
Eigen::Matrix<T, 3, 3> skew_sym_mat;
skew_sym_mat<<0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0;
return skew_sym_mat;
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &&ang)
{
T ang_norm = ang.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang / ang_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
/// Roderigous Tranformation
return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T, typename Ts>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &ang_vel, const Ts &dt)
{
T ang_vel_norm = ang_vel.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_vel_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang_vel / ang_vel_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
T r_ang = ang_vel_norm * dt;
/// Roderigous Tranformation
return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const T &v1, const T &v2, const T &v3)
{
T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3);
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (norm > 0.00001)
{
T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm};
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_ang);
/// Roderigous Tranformation
return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K;
}
else
{
return Eye3;
}
}
/* Logrithm of a Rotation Matrix */
template<typename T>
Eigen::Matrix<T,3,1> Log(const Eigen::Matrix<T, 3, 3> &R)
{
T theta = (R.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (R.trace() - 1));
Eigen::Matrix<T,3,1> K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1));
return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K);
}
template<typename T>
Eigen::Matrix<T, 3, 1> RotMtoEuler(const Eigen::Matrix<T, 3, 3> &rot)
{
T sy = sqrt(rot(0,0)*rot(0,0) + rot(1,0)*rot(1,0));
bool singular = sy < 1e-6;
T x, y, z;
if(!singular)
{
x = atan2(rot(2, 1), rot(2, 2));
y = atan2(-rot(2, 0), sy);
z = atan2(rot(1, 0), rot(0, 0));
}
else
{
x = atan2(-rot(1, 2), rot(1, 1));
y = atan2(-rot(2, 0), sy);
z = 0;
}
Eigen::Matrix<T, 3, 1> ang(x, y, z);
return ang;
}
#endif

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src/Point-LIO/launch/gdb_debug_example.launch Executable file
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<launch>
<!-- Launch file for Livox AVIA LiDAR -->
<arg name="rviz" default="true" />
<node pkg="point_lio" type="pointlio_mapping" name="laserMapping" output="screen" required="true" launch-prefix="gdb -ex run --args">
<rosparam command="load" file="$(find point_lio)/config/avia.yaml" />
<param name="use_imu_as_input" type="bool" value="0"/> <!--change to 1 to use IMU as input of Point-LIO-->
<param name="prop_at_freq_of_imu" type="bool" value="1"/>
<param name="check_satu" type="bool" value="1"/>
<param name="init_map_size" type="int" value="10"/>
<param name="point_filter_num" type="int" value="1"/> <!--4, 3-->
<param name="space_down_sample" type="bool" value="1" />
<param name="filter_size_surf" type="double" value="0.3" /> <!--0.5, 0.3, 0.2, 0.15, 0.1-->
<param name="filter_size_map" type="double" value="0.2" /> <!--0.5, 0.3, 0.15, 0.1-->
<param name="cube_side_length" type="double" value="1000" /> <!--2000-->
<param name="runtime_pos_log_enable" type="bool" value="0" /> <!--1-->
</node>
<group if="$(arg rviz)">
<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find point_lio)/rviz_cfg/loam_livox.rviz" />
</group>
launch-prefix="gdb -ex run --args"
</launch>

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