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Author SHA1 Message Date
096e317353 r1-7-12 2025-07-12 22:45:51 +08:00
500f6bd1d8 r1-7-12 2025-07-12 22:39:19 +08:00
2f3e6caa8d r1-7-12 2025-07-12 22:13:39 +08:00
ccb4a91e64 提交一下 2025-07-11 00:48:19 +08:00
dcef535b33 R1 2025-07-10 13:21:45 +08:00
75 changed files with 1685 additions and 757 deletions

44
.vscode/c_cpp_properties.json vendored Normal file
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@ -0,0 +1,44 @@
{
"configurations": [
{
"browse": {
"databaseFilename": "${default}",
"limitSymbolsToIncludedHeaders": false
},
"includePath": [
"/home/robofish/RC2025/install/teb_local_planner/include/**",
"/home/robofish/RC2025/install/teb_msgs/include/**",
"/home/robofish/RC2025/install/ros2_livox_simulation/include/**",
"/home/robofish/RC2025/install/rm_msgs/include/**",
"/home/robofish/RC2025/install/pointcloud_to_laserscan/include/**",
"/home/robofish/RC2025/install/icp_registration/include/**",
"/home/robofish/RC2025/install/fast_lio/include/**",
"/home/robofish/RC2025/install/livox_ros_driver2/include/**",
"/home/robofish/RC2025/install/linefit_ground_segmentation/include/**",
"/home/robofish/RC2025/install/imu_complementary_filter/include/**",
"/home/robofish/RC2025/install/fake_vel_transform/include/**",
"/home/robofish/RC2025/install/costmap_converter_msgs/include/**",
"/opt/ros/humble/include/**",
"/home/robofish/RC2025/src/rm_driver/rm_serial_driver/include/**",
"/home/robofish/RC2025/src/rm_localization/FAST_LIO/include/**",
"/home/robofish/RC2025/src/rm_localization/icp_registration/include/**",
"/home/robofish/RC2025/src/rm_localization/point_lio/include/**",
"/home/robofish/RC2025/src/rm_navigation/costmap_converter/costmap_converter/include/**",
"/home/robofish/RC2025/src/rm_navigation/fake_vel_transform/include/**",
"/home/robofish/RC2025/src/rm_navigation/teb_local_planner/teb_local_planner/include/**",
"/home/robofish/RC2025/src/rm_perception/imu_complementary_filter/include/**",
"/home/robofish/RC2025/src/rm_perception/linefit_ground_segementation_ros2/linefit_ground_segmentation/include/**",
"/home/robofish/RC2025/src/rm_perception/pointcloud_to_laserscan/include/**",
"/home/robofish/RC2025/src/rm_simpal_move/include/**",
"/home/robofish/RC2025/src/rm_simulation/livox_laser_simulation_RO2/include/**",
"/usr/include/**"
],
"name": "ROS",
"intelliSenseMode": "gcc-x64",
"compilerPath": "/usr/bin/gcc",
"cStandard": "gnu11",
"cppStandard": "c++14"
}
],
"version": 4
}

21
.vscode/settings.json vendored Normal file
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@ -0,0 +1,21 @@
{
"cmake.sourceDirectory": "/home/robofish/RC2025/src/rm_msg",
"python.autoComplete.extraPaths": [
"/home/robofish/RC2025/install/teb_msgs/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/rm_msgs/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/fast_lio/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/livox_ros_driver2/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/costmap_converter_msgs/local/lib/python3.10/dist-packages",
"/opt/ros/humble/lib/python3.10/site-packages",
"/opt/ros/humble/local/lib/python3.10/dist-packages"
],
"python.analysis.extraPaths": [
"/home/robofish/RC2025/install/teb_msgs/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/rm_msgs/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/fast_lio/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/livox_ros_driver2/local/lib/python3.10/dist-packages",
"/home/robofish/RC2025/install/costmap_converter_msgs/local/lib/python3.10/dist-packages",
"/opt/ros/humble/lib/python3.10/site-packages",
"/opt/ros/humble/local/lib/python3.10/dist-packages"
]
}

221
README.md
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@ -1,221 +0,0 @@
# 🎯 RC2025 自动定位瞄准代码
> 基于ROS2的机器人自动定位与瞄准系统支持激光雷达建图和导航功能
[![ROS2](https://img.shields.io/badge/ROS2-Humble-blue.svg)](https://docs.ros.org/en/humble/)
[![Ubuntu](https://img.shields.io/badge/Ubuntu-22.04-orange.svg)](https://ubuntu.com/)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
## 📋 目录
- [系统要求](#系统要求)
- [环境配置](#环境配置)
- [快速开始](#快速开始)
- [重要参数配置](#重要参数配置)
- [故障排除](#故障排除)
- [贡献指南](#贡献指南)
## 🔧 系统要求
| 组件 | 版本/型号 |
|------|-----------|
| 操作系统 | Ubuntu 22.04 LTS |
| ROS版本 | ROS2 Humble |
| 激光雷达 | Livox MID360 |
| 处理器 | x86_64 (推荐) |
| 内存 | 8GB+ (推荐) |
## 🚀 环境配置
### 1. 安装 Livox SDK2
```bash
# 安装依赖
sudo apt update
sudo apt install cmake build-essential
# 克隆并编译 Livox SDK2
git clone https://github.com/Livox-SDK/Livox-SDK2.git
cd ./Livox-SDK2/
mkdir build && cd build
cmake .. && make -j$(nproc)
sudo make install
```
### 2. 安装串口驱动
```bash
pip install pyserial
```
### 3. 安装ROS2依赖
```bash
# 进入工作区
cd /Users/lvzucheng/Documents/R/RC2025
# 安装依赖包
rosdep install -r --from-paths src --ignore-src --rosdistro $ROS_DISTRO -y
```
## 🎯 快速开始
### 1. 编译项目
```bash
. build.sh
```
### 2. 🗺️ 建图模式
用于创建环境地图和点云数据:
```bash
. mapping.sh
```
#### 建图前配置
1. **修改地图保存文件名**
```bash
# 编辑 mapping.sh
nano mapping.sh
# 将 'RC2025' 改为您的项目名
```
2. **同步修改点云文件配置**
```bash
# 编辑 FAST-LIO 配置文件
nano src/rm_nav_bringup/config/reality/fastlio_mid360_real.yaml
# 确保 pcd 文件名与 mapping.sh 中一致
```
#### 建图操作步骤
1. **启动建图程序**
```bash
./mapping.sh
```
2. **控制机器人移动**
- 使用遥控器或键盘控制机器人
- 确保覆盖所有需要建图的区域
3. **保存点云文件**
```bash
ros2 service call /map_save std_srvs/srv/Trigger
```
4. **保存地图文件**
- 在RViz中使用地图保存功能
- 确保地图名称保持一致
#### 建图效果展示
<details>
<summary>点击查看建图效果图</summary>
![建图效果1](doc/img/07b3c725_11812035.png)
![建图效果2](doc/img/5032aa1d_11812035.png)
![建图效果3](doc/img/bea7dae2_11812035.jpeg)
</details>
### 3. 🧭 导航模式
使用已建立的地图进行导航:
```bash
chmod +x nav.sh
./nav.sh
```
#### 导航操作说明
1. **启动导航程序**
2. **在RViz中设置初始位置**
3. **设置目标点进行导航**
4. **监控导航状态**
## ⚙️ 重要参数配置
### 📍 激光雷达安装位置
#### 位置参数配置
```yaml
# 文件src/rm_nav_bringup/config/reality/measurement_params_real.yaml
translation:
x: 0.0 # 前后位置 (m)
y: 0.0 # 左右位置 (m)
z: 0.0 # 上下位置 (m)
```
> ⚠️ **注意**:不要修改 `rpy` 参数
#### 姿态参数配置
```json
// 文件src/rm_nav_bringup/config/reality/MID360_config.json
{
"yaw": 0.0, // 偏航角
"pitch": 0.0, // 俯仰角
"roll": 0.0 // 翻滚角
}
```
> ⚠️ **注意**:不要修改 `xyz` 参数
### 🌍 地面点云分割
```yaml
# 文件src/rm_nav_bringup/config/reality/segmentation_real.yaml
sensor_height: 0.3 # 激光雷达距离地面的高度 (m)
max_dist_to_line: 0.05 # 地面点云分割的最低高度 (m)
```
### 🎯 目标点设定
```bash
# 文件nav.sh
# 篮筐目标点坐标
TARGET_X=1.0 # X坐标
TARGET_Y=0.0 # Y坐标
```
## 🔧 故障排除
### 常见问题
<details>
<summary>激光雷达无法连接</summary>
1. 检查网络连接
2. 确认IP地址配置
3. 检查防火墙设置
4. 验证SDK安装
</details>
<details>
<summary>建图效果不佳</summary>
1. 检查激光雷达安装位置
2. 调整地面分割参数
3. 确保移动速度适中
4. 检查环境光照条件
</details>
<details>
<summary>导航精度不够</summary>
1. 重新标定雷达参数
2. 优化地图质量
3. 调整导航参数
4. 检查里程计数据
</details>
## 📝 使用技巧
- **建图时**:保持稳定的移动速度,避免急转急停
- **导航时**:确保地图与实际环境一致
- **调试时**使用RViz可视化工具监控状态
- **维护时**:定期更新地图数据

4
mapping.sh Normal file → Executable file
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@ -2,11 +2,11 @@ source install/setup.bash
commands=( commands=(
"ros2 launch rm_nav_bringup bringup_real.launch.py \ "ros2 launch rm_nav_bringup bringup_real.launch.py \
world:=RC2026 \ world:=test \
mode:=mapping \ mode:=mapping \
lio:=fastlio \ lio:=fastlio \
localization:=icp \ localization:=icp \
lio_rviz:=false \ lio_rviz:=true \
nav_rviz:=true" nav_rviz:=true"
) )

10
nav.sh
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@ -1,7 +1,7 @@
# 备场代码
source install/setup.bash source install/setup.bash
commands=( commands=(
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/pub_aim.py"
"ros2 launch rm_nav_bringup bringup_real.launch.py \ "ros2 launch rm_nav_bringup bringup_real.launch.py \
world:=RC2026 \ world:=RC2026 \
mode:=nav \ mode:=nav \
@ -10,7 +10,13 @@ commands=(
lio_rviz:=false \ lio_rviz:=false \
nav_rviz:=true" nav_rviz:=true"
"ros2 launch rm_simpal_move simple_move.launch.py" "ros2 launch rm_simpal_move simple_move.launch.py"
"ros2 topic pub /move_goal rm_msgs/msg/MoveGoal '{x: 0.60, y: 3.995, angle: 0.0, max_speed: 10.0, tolerance: 0.1, rotor: false}' --once" "/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/R2_Serial.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/receive.py"
# "/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/slect.py map"
"/bin/python3 /home/robofish/RC2025/src/rc_lidar/pcd2pgm.py"
"/bin/python3 /home/robofish/RC2025/src/rc_lidar/caijian.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/pub_goal.py"
) )
for cmd in "${commands[@]}"; do for cmd in "${commands[@]}"; do

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nav1.sh Normal file
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# 备场代码
source install/setup.bash
commands=(
"ros2 launch rm_nav_bringup bringup_real.launch.py \
world:=map1 \
mode:=nav \
lio:=fastlio \
localization:=icp \
lio_rviz:=false \
nav_rviz:=true"
"ros2 launch rm_simpal_move simple_move.launch.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/R2_Serial.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/receive.py"
# "/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/slect.py map1"
"/bin/python3 /home/robofish/RC2025/src/rc_lidar/pcd2pgm.py"
"/bin/python3 /home/robofish/RC2025/src/rc_lidar/caijian.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/pub_goal.py"
)
for cmd in "${commands[@]}"; do
gnome-terminal -- bash -c "source install/setup.bash; $cmd; exec bash"
sleep 0.5
done

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nav2.sh Normal file
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# 备场代码
source install/setup.bash
commands=(
"ros2 launch rm_nav_bringup bringup_real.launch.py \
world:=map2 \
mode:=nav \
lio:=fastlio \
localization:=icp \
lio_rviz:=false \
nav_rviz:=true"
"ros2 launch rm_simpal_move simple_move.launch.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/R2_Serial.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/receive.py"
# "/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/slect.py map2"
"/bin/python3 /home/robofish/RC2025/src/rc_lidar/pcd2pgm.py"
"/bin/python3 /home/robofish/RC2025/src/rc_lidar/caijian.py"
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/pub_goal.py"
)
for cmd in "${commands[@]}"; do
gnome-terminal -- bash -c "source install/setup.bash; $cmd; exec bash"
sleep 0.5
done

19
r1.sh
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@ -1,19 +0,0 @@
source install/setup.bash
commands=(
"/bin/python3 /home/robofish/RC2025/src/rm_driver/rm_serial_driver/script/pub_aim.py"
"ros2 launch rm_nav_bringup bringup_real.launch.py \
world:=RC2025 \
mode:=nav \
lio:=fastlio \
localization:=icp \
lio_rviz:=false \
nav_rviz:=true"
"ros2 launch rm_simpal_move simple_move.launch.py"
"ros2 topic pub /move_goal rm_msgs/msg/MoveGoal '{x: 0.56, y: 3.960, angle: 0.0, max_speed: 10.0, tolerance: 0.1, rotor: false}' --once"
)
for cmd in "${commands[@]}"; do
gnome-terminal -- bash -c "source install/setup.bash; $cmd; exec bash"
sleep 0.5
done

63
src/rc_lidar/caijian.py Normal file
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#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2, PointField
import numpy as np
import struct
from sklearn.cluster import DBSCAN
class LidarFilterNode(Node):
def __init__(self):
super().__init__('caijian_node')
self.publisher_ = self.create_publisher(PointCloud2, '/livox/lidar_filtered', 10)
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar/pointcloud',
self.filter_callback,
10)
self.get_logger().info('caijian_node started, numpy filtering z in [1.5,3]m, distance<=12m, remove isolated points')
def filter_callback(self, msg):
num_points = msg.width * msg.height
data = np.frombuffer(msg.data, dtype=np.uint8)
points = np.zeros((num_points, 4), dtype=np.float32) # x, y, z, intensity
for i in range(num_points):
offset = i * msg.point_step
x = struct.unpack_from('f', data, offset)[0]
y = struct.unpack_from('f', data, offset + 4)[0]
z = struct.unpack_from('f', data, offset + 8)[0]
intensity = struct.unpack_from('f', data, offset + 12)[0]
points[i] = [x, y, z, intensity]
z_mask = (points[:,2] >= 1.5) & (points[:,2] <= 3.0)
dist_mask = np.linalg.norm(points[:,:3], axis=1) <= 16.0
mask = z_mask & dist_mask
filtered_points = points[mask]
# 使用DBSCAN去除孤立点
if filtered_points.shape[0] > 0:
clustering = DBSCAN(eps=0.3, min_samples=5).fit(filtered_points[:,:3])
core_mask = clustering.labels_ != -1
filtered_points = filtered_points[core_mask]
fields = [
PointField(name='x', offset=0, datatype=PointField.FLOAT32, count=1),
PointField(name='y', offset=4, datatype=PointField.FLOAT32, count=1),
PointField(name='z', offset=8, datatype=PointField.FLOAT32, count=1),
PointField(name='intensity', offset=12, datatype=PointField.FLOAT32, count=1),
]
filtered_points_list = filtered_points.tolist()
import sensor_msgs_py.point_cloud2 as pc2
filtered_msg = pc2.create_cloud(msg.header, fields, filtered_points_list)
self.publisher_.publish(filtered_msg)
def main(args=None):
rclpy.init(args=args)
node = LidarFilterNode()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

90
src/rc_lidar/circlr.py Normal file
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import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
from geometry_msgs.msg import PointStamped
from sensor_msgs_py import point_cloud2 as pc2
import numpy as np
from sklearn.cluster import DBSCAN
import time
def statistical_outlier_removal(points, k=20, std_ratio=2.0):
from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=k+1).fit(points)
distances, _ = nbrs.kneighbors(points)
mean_dist = np.mean(distances[:, 1:], axis=1)
threshold = np.mean(mean_dist) + std_ratio * np.std(mean_dist)
mask = mean_dist < threshold
return points[mask]
class HoopFinder(Node):
def __init__(self):
super().__init__('find_hoop')
self.sub = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.callback,
10)
self.pub = self.create_publisher(PointStamped, '/hoop_position', 10)
self.buffer = []
self.start_time = None
self.hoop_history = []
def callback(self, msg):
# 采集0.4秒内的点云
if self.start_time is None:
self.start_time = time.time()
for p in pc2.read_points(msg, field_names=("x", "y", "z", "intensity"), skip_nans=True):
self.buffer.append([p[0], p[1], p[2], p[3]])
if time.time() - self.start_time < 0.4:
return
points = np.array(self.buffer)
self.buffer = []
self.start_time = None
# 高度滤波
filtered = points[(points[:,2] > 1.0) & (points[:,2] < 3.0)]
if len(filtered) == 0:
return
# 统计离群点滤波
filtered = statistical_outlier_removal(filtered[:,:3], k=20, std_ratio=2.0)
# DBSCAN聚类
clustering = DBSCAN(eps=0.3, min_samples=10).fit(filtered)
labels = clustering.labels_
unique_labels = set(labels)
hoop_pos = None
max_cluster_size = 0
for label in unique_labels:
if label == -1:
continue
cluster = filtered[labels == label]
if len(cluster) > max_cluster_size:
max_cluster_size = len(cluster)
hoop_pos = np.mean(cluster, axis=0)
# 均值滤波输出
if hoop_pos is not None:
self.hoop_history.append(hoop_pos)
if len(self.hoop_history) > 5:
self.hoop_history.pop(0)
smooth_pos = np.mean(self.hoop_history, axis=0)
pt = PointStamped()
pt.header = msg.header
pt.point.x = float(smooth_pos[0])
pt.point.y = float(smooth_pos[1])
pt.point.z = float(smooth_pos[2])
self.pub.publish(pt)
self.get_logger().info(f"Hoop position (smoothed): {smooth_pos}")
def main(args=None):
rclpy.init(args=args)
node = HoopFinder()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

59
src/rc_lidar/find.py Normal file
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@ -0,0 +1,59 @@
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
from geometry_msgs.msg import PointStamped
from sensor_msgs_py import point_cloud2 as pc2
import numpy as np
from sklearn.cluster import DBSCAN
class HoopFinder(Node):
def __init__(self):
super().__init__('find_hoop')
self.sub = self.create_subscription(
PointCloud2,
'/livox/lidar',
self.callback,
10)
self.pub = self.create_publisher(PointStamped, '/hoop_position', 10)
def callback(self, msg):
points = []
for p in pc2.read_points(msg, field_names=("x", "y", "z", "intensity"), skip_nans=True):
points.append([p[0], p[1], p[2], p[3]])
points = np.array(points)
filtered = points[(points[:,2] > 1.0) & (points[:,2] < 3.0)]
if len(filtered) == 0:
return
clustering = DBSCAN(eps=0.3, min_samples=10).fit(filtered[:,:3])
labels = clustering.labels_
unique_labels = set(labels)
hoop_pos = None
max_cluster_size = 0
for label in unique_labels:
if label == -1:
continue
cluster = filtered[labels == label]
if len(cluster) > max_cluster_size:
max_cluster_size = len(cluster)
hoop_pos = np.mean(cluster[:,:3], axis=0)
if hoop_pos is not None:
pt = PointStamped()
pt.header = msg.header
pt.point.x = float(hoop_pos[0])
pt.point.y = float(hoop_pos[1])
pt.point.z = float(hoop_pos[2])
self.pub.publish(pt)
self.get_logger().info(f"Hoop position: {hoop_pos}")
def main(args=None):
rclpy.init(args=args)
node = HoopFinder()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

63
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#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2, PointField
import numpy as np
import struct
from sklearn.cluster import DBSCAN
class LidarFilterNode(Node):
def __init__(self):
super().__init__('caijian_node')
self.publisher_ = self.create_publisher(PointCloud2, '/livox/lidar_filtered', 10)
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar/pointcloud',
self.filter_callback,
10)
self.get_logger().info('caijian_node started, numpy filtering z in [1.5,3]m, distance<=12m, remove isolated points')
def filter_callback(self, msg):
num_points = msg.width * msg.height
data = np.frombuffer(msg.data, dtype=np.uint8)
points = np.zeros((num_points, 4), dtype=np.float32) # x, y, z, intensity
for i in range(num_points):
offset = i * msg.point_step
x = struct.unpack_from('f', data, offset)[0]
y = struct.unpack_from('f', data, offset + 4)[0]
z = struct.unpack_from('f', data, offset + 8)[0]
intensity = struct.unpack_from('f', data, offset + 12)[0]
points[i] = [x, y, z, intensity]
z_mask = (points[:,2] >= 1.5) & (points[:,2] <= 3.0)
dist_mask = np.linalg.norm(points[:,:3], axis=1) <= 12.0
mask = z_mask & dist_mask
filtered_points = points[mask]
# 使用DBSCAN去除孤立点
if filtered_points.shape[0] > 0:
clustering = DBSCAN(eps=0.3, min_samples=5).fit(filtered_points[:,:3])
core_mask = clustering.labels_ != -1
filtered_points = filtered_points[core_mask]
fields = [
PointField(name='x', offset=0, datatype=PointField.FLOAT32, count=1),
PointField(name='y', offset=4, datatype=PointField.FLOAT32, count=1),
PointField(name='z', offset=8, datatype=PointField.FLOAT32, count=1),
PointField(name='intensity', offset=12, datatype=PointField.FLOAT32, count=1),
]
filtered_points_list = filtered_points.tolist()
import sensor_msgs_py.point_cloud2 as pc2
filtered_msg = pc2.create_cloud(msg.header, fields, filtered_points_list)
self.publisher_.publish(filtered_msg)
def main(args=None):
rclpy.init(args=args)
node = LidarFilterNode()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2, PointField
import numpy as np
import struct
from sklearn.cluster import DBSCAN
import cv2
from visualization_msgs.msg import Marker
from sklearn.linear_model import RANSACRegressor
def ransac_line_3d(points, threshold=0.05, min_inliers=20):
best_inliers = []
best_line = None
N = len(points)
if N < min_inliers:
return None, []
for _ in range(100):
idx = np.random.choice(N, 2, replace=False)
p1, p2 = points[idx]
v = p2 - p1
v = v / np.linalg.norm(v)
dists = np.linalg.norm(np.cross(points - p1, v), axis=1)
inliers = np.where(dists < threshold)[0]
if len(inliers) > len(best_inliers):
best_inliers = inliers
best_line = (p1, p2)
if len(best_inliers) > N * 0.5:
break
return best_line, best_inliers
def fit_rectangle_pca(cluster):
# 用PCA找主方向和边界点
pts = cluster[:, :3]
mean = np.mean(pts, axis=0)
cov = np.cov(pts.T)
eigvals, eigvecs = np.linalg.eigh(cov)
order = np.argsort(eigvals)[::-1]
main_dir = eigvecs[:, order[0]]
second_dir = eigvecs[:, order[1]]
# 投影到主方向和次方向
proj_main = np.dot(pts - mean, main_dir)
proj_second = np.dot(pts - mean, second_dir)
# 找四个角点
corners = []
for xm in [np.min(proj_main), np.max(proj_main)]:
for xs in [np.min(proj_second), np.max(proj_second)]:
idx = np.argmin((proj_main - xm)**2 + (proj_second - xs)**2)
corners.append(pts[idx])
return corners
def rectangle_score(pts):
# 评估4个点是否接近矩形
d = [np.linalg.norm(pts[i] - pts[(i+1)%4]) for i in range(4)]
diag1 = np.linalg.norm(pts[0] - pts[2])
diag2 = np.linalg.norm(pts[1] - pts[3])
w = max(d)
h = min(d)
ratio = w / h if h > 0 else 0
ideal_ratio = 1.8 / 1.05
score = abs(ratio - ideal_ratio) + abs(diag1 - diag2) / max(diag1, diag2)
return score
def classify_lines(lines):
# lines: 每条线是 [x, y, z, intensity]
# 假设 lines 是8个端点两两为一条线
vertical_lines = []
horizontal_lines = []
for i in range(0, len(lines), 2):
p1 = np.array(lines[i][:3])
p2 = np.array(lines[i+1][:3])
vec = p2 - p1
length = np.linalg.norm(vec)
# 计算与地面的夹角假设地面为z轴为0垂直为z方向
dz = abs(vec[2])
dxy = np.linalg.norm(vec[:2])
# 垂直线z方向分量大长度约1.05m
if dz > dxy and 0.95 < length < 1.15:
vertical_lines.append((i, i+1, length))
# 水平线z方向分量小长度约1.8m
elif dz < dxy and 1.7 < length < 1.9:
horizontal_lines.append((i, i+1, length))
return vertical_lines, horizontal_lines
def find_best_rectangle_from_lines(lines):
vertical_lines, horizontal_lines = classify_lines(lines)
# 只选出2条垂直线和2条水平线
if len(vertical_lines) < 2 or len(horizontal_lines) < 2:
return None
# 取长度最接近目标的两条
vertical_lines = sorted(vertical_lines, key=lambda x: abs(x[2]-1.05))[:2]
horizontal_lines = sorted(horizontal_lines, key=lambda x: abs(x[2]-1.8))[:2]
# 组合4个端点
indices = [vertical_lines[0][0], vertical_lines[0][1],
vertical_lines[1][0], vertical_lines[1][1],
horizontal_lines[0][0], horizontal_lines[0][1],
horizontal_lines[1][0], horizontal_lines[1][1]]
# 去重只保留4个顶点
unique_indices = list(set(indices))
if len(unique_indices) < 4:
return None
pts = [lines[idx] for idx in unique_indices[:4]]
# 按矩形评分排序
from itertools import permutations
best_score = float('inf')
best_rect = None
for order in permutations(range(4)):
ordered = [pts[i] for i in order]
score = rectangle_score(np.array([p[:3] for p in ordered]))
if score < best_score:
best_score = score
best_rect = ordered
return best_rect
class BasketballFrameDetector(Node):
def __init__(self):
super().__init__('basketball_frame_detector')
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.pointcloud_callback,
10
)
self.publisher = self.create_publisher(
PointCloud2,
'/basketball_frame_cloud',
10
)
self.marker_pub = self.create_publisher(
Marker,
'/basketball_frame_lines',
10
)
self.pointcloud_buffer = []
self.max_buffer_size = 10 # 减少缓冲帧数,加快响应
self.center_buffer = []
self.center_buffer_size = 5
def pointcloud_callback(self, msg):
points = self.pointcloud2_to_xyz(msg)
if points.shape[0] == 0:
return # 跳过空点云
self.pointcloud_buffer.append(points)
# 只保留非空点云
self.pointcloud_buffer = [arr for arr in self.pointcloud_buffer if arr.shape[0] > 0]
if len(self.pointcloud_buffer) > self.max_buffer_size:
self.pointcloud_buffer.pop(0)
all_points = np.vstack(self.pointcloud_buffer)
xy_points = all_points[:, :2]
if len(xy_points) < 10:
self.get_logger().info('点数太少,跳过')
return
clustering = DBSCAN(eps=0.3, min_samples=10).fit(xy_points)
labels = clustering.labels_
unique_labels = set(labels)
found = False
for label in unique_labels:
if label == -1:
continue
cluster = all_points[labels == label]
if len(cluster) < 30:
continue
min_x, min_y = np.min(cluster[:, :2], axis=0)
max_x, max_y = np.max(cluster[:, :2], axis=0)
width = abs(max_x - min_x)
height = abs(max_y - min_y)
if 1.5 < width < 2.1 and 0.7 < height < 1.3:
cloud_msg = self.xyz_array_to_pointcloud2(cluster, msg.header)
self.publisher.publish(cloud_msg)
# 用PCA直接找矩形四角
corners = fit_rectangle_pca(cluster)
from itertools import permutations
best_score = float('inf')
best_rect = None
for order in permutations(range(4)):
ordered = [corners[i] for i in order]
score = rectangle_score(np.array(ordered))
if score < best_score:
best_score = score
best_rect = ordered
rect_lines = best_rect
if rect_lines is None or len(rect_lines) < 4:
self.get_logger().info('未找到合适矩形')
continue
# 发布最新的矩形
marker = Marker()
marker.header = msg.header
marker.ns = "basketball_frame"
marker.id = 0
marker.type = Marker.LINE_LIST
marker.action = Marker.ADD
marker.scale.x = 0.08
marker.color.r = 0.0
marker.color.g = 1.0
marker.color.b = 0.0
marker.color.a = 1.0
marker.points = []
from geometry_msgs.msg import Point
for i in range(4):
p1 = rect_lines[i]
p2 = rect_lines[(i+1)%4]
pt1 = Point(x=float(p1[0]), y=float(p1[1]), z=float(p1[2]))
pt2 = Point(x=float(p2[0]), y=float(p2[1]), z=float(p2[2]))
marker.points.append(pt1)
marker.points.append(pt2)
self.marker_pub.publish(marker)
# 分别发布4条最优边线
for i in range(4):
edge_marker = Marker()
edge_marker.header = msg.header
edge_marker.ns = "basketball_frame_edges"
edge_marker.id = i
edge_marker.type = Marker.LINE_STRIP
edge_marker.action = Marker.ADD
edge_marker.scale.x = 0.12
colors = [
(1.0, 0.0, 0.0),
(0.0, 1.0, 0.0),
(0.0, 0.0, 1.0),
(1.0, 1.0, 0.0)
]
edge_marker.color.r = colors[i][0]
edge_marker.color.g = colors[i][1]
edge_marker.color.b = colors[i][2]
edge_marker.color.a = 1.0
pt1 = Point(x=float(rect_lines[i][0]), y=float(rect_lines[i][1]), z=float(rect_lines[i][2]))
pt2 = Point(x=float(rect_lines[(i+1)%4][0]), y=float(rect_lines[(i+1)%4][1]), z=float(rect_lines[(i+1)%4][2]))
edge_marker.points = [pt1, pt2]
self.marker_pub.publish(edge_marker)
# 中心点滑动平均
center = np.mean(np.array(rect_lines), axis=0)
self.center_buffer.append(center)
if len(self.center_buffer) > self.center_buffer_size:
self.center_buffer.pop(0)
stable_center = np.mean(self.center_buffer, axis=0)
# 发布中心点Marker
center_marker = Marker()
center_marker.header = msg.header
center_marker.ns = "basketball_frame"
center_marker.id = 1
center_marker.type = Marker.SPHERE
center_marker.action = Marker.ADD
center_marker.scale.x = 0.15
center_marker.scale.y = 0.15
center_marker.scale.z = 0.15
center_marker.color.r = 1.0
center_marker.color.g = 0.0
center_marker.color.b = 0.0
center_marker.color.a = 1.0
center_marker.pose.position.x = float(stable_center[0])
center_marker.pose.position.y = float(stable_center[1])
center_marker.pose.position.z = float(stable_center[2])
center_marker.pose.orientation.x = 0.0
center_marker.pose.orientation.y = 0.0
center_marker.pose.orientation.z = 0.0
center_marker.pose.orientation.w = 1.0
self.marker_pub.publish(center_marker)
# 计算法向量篮板主方向PCA最小特征值方向
pts = np.array(rect_lines)
mean = np.mean(pts, axis=0)
cov = np.cov(pts.T)
eigvals, eigvecs = np.linalg.eigh(cov)
order = np.argsort(eigvals)[::-1]
normal_vec = eigvecs[:, order[2]]
normal_vec = normal_vec / np.linalg.norm(normal_vec)
# 向内侧偏移30cm
offset_point = stable_center + 0.3 * normal_vec
# 发布偏移点Marker
offset_marker = Marker()
offset_marker.header = msg.header
offset_marker.ns = "basketball_frame"
offset_marker.id = 2
offset_marker.type = Marker.SPHERE
offset_marker.action = Marker.ADD
offset_marker.scale.x = 0.12
offset_marker.scale.y = 0.12
offset_marker.scale.z = 0.12
offset_marker.color.r = 0.0
offset_marker.color.g = 0.0
offset_marker.color.b = 1.0
offset_marker.color.a = 1.0
offset_marker.pose.position.x = float(offset_point[0])
offset_marker.pose.position.y = float(offset_point[1])
offset_marker.pose.position.z = float(offset_point[2])
offset_marker.pose.orientation.x = 0.0
offset_marker.pose.orientation.y = 0.0
offset_marker.pose.orientation.z = 0.0
offset_marker.pose.orientation.w = 1.0
self.marker_pub.publish(offset_marker)
found = True
break
if not found:
self.get_logger().info('本帧未找到矩形')
def pointcloud2_to_xyz(self, cloud_msg):
fmt = 'ffff'
points = []
for i in range(cloud_msg.width):
offset = i * cloud_msg.point_step
x, y, z, intensity = struct.unpack_from(fmt, cloud_msg.data, offset)
points.append([x, y, z, intensity])
return np.array(points)
def xyz_array_to_pointcloud2(self, points, header):
msg = PointCloud2()
msg.header = header
msg.height = 1
msg.width = len(points)
msg.is_dense = True
msg.is_bigendian = False
msg.point_step = 16
msg.row_step = msg.point_step * msg.width
msg.fields = [
PointField(name='x', offset=0, datatype=7, count=1),
PointField(name='y', offset=4, datatype=7, count=1),
PointField(name='z', offset=8, datatype=7, count=1),
PointField(name='intensity', offset=12, datatype=7, count=1),
]
msg.data = b''.join([struct.pack('ffff', *p) for p in points])
return msg
def main(args=None):
rclpy.init(args=args)
node = BasketballFrameDetector()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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src/rc_lidar/line.py Normal file
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import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
from nav_msgs.msg import OccupancyGrid
import numpy as np
import struct
import time
class PointCloudToGrid(Node):
def __init__(self):
super().__init__('pointcloud_to_grid')
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.pointcloud_callback,
10)
self.publisher = self.create_publisher(OccupancyGrid, '/lidar_grid', 10)
self.grid_size = 2000
self.resolution = 0.02
self.origin_x = -20.0
self.origin_y = -20.0
self.points_buffer = []
self.last_header = None
# 定时器每0.5秒触发一次
self.timer = self.create_timer(0.5, self.publish_grid)
def pointcloud_callback(self, msg):
points = self.pointcloud2_to_xyz_array(msg)
self.points_buffer.append(points)
self.last_header = msg.header # 保存最新header用于地图消息
def publish_grid(self):
if not self.points_buffer:
return
# 合并0.5秒内所有点
all_points = np.concatenate(self.points_buffer, axis=0)
grid = np.zeros((self.grid_size, self.grid_size), dtype=np.int8)
for x, y, z in all_points:
if z < 2.0:
ix = int((x - self.origin_x) / self.resolution)
iy = int((y - self.origin_y) / self.resolution)
if 0 <= ix < self.grid_size and 0 <= iy < self.grid_size:
grid[iy, ix] = 100
grid_msg = OccupancyGrid()
if self.last_header:
grid_msg.header = self.last_header
grid_msg.info.resolution = self.resolution
grid_msg.info.width = self.grid_size
grid_msg.info.height = self.grid_size
grid_msg.info.origin.position.x = self.origin_x
grid_msg.info.origin.position.y = self.origin_y
grid_msg.data = grid.flatten().tolist()
self.publisher.publish(grid_msg)
self.points_buffer.clear() # 清空缓存
def pointcloud2_to_xyz_array(self, cloud_msg):
# 解析 PointCloud2 数据为 numpy 数组
fmt = 'fff' # x, y, z
point_step = cloud_msg.point_step
data = cloud_msg.data
points = []
for i in range(0, len(data), point_step):
x, y, z = struct.unpack_from(fmt, data, i)
points.append([x, y, z])
return np.array(points)
def main(args=None):
rclpy.init(args=args)
node = PointCloudToGrid()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
import numpy as np
import open3d as o3d
import time
class PointCloudSaver(Node):
def __init__(self):
super().__init__('pcd_saver')
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.listener_callback,
10)
self.point_clouds = []
self.start_time = time.time()
self.timer = self.create_timer(3.0, self.save_and_exit)
self.saving = False
def listener_callback(self, msg):
if not self.saving:
pc = self.pointcloud2_to_xyz_array(msg)
if pc is not None:
self.point_clouds.append(pc)
def pointcloud2_to_xyz_array(self, cloud_msg):
# 仅支持 x, y, z 均为 float32 且无 padding 的点云
import struct
points = []
point_step = cloud_msg.point_step
for i in range(cloud_msg.width * cloud_msg.height):
offset = i * point_step
x, y, z = struct.unpack_from('fff', cloud_msg.data, offset)
points.append([x, y, z])
return np.array(points)
def save_and_exit(self):
if not self.saving:
self.saving = True
if self.point_clouds:
all_points = np.vstack(self.point_clouds)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(all_points)
o3d.io.write_point_cloud("output.pcd", pcd)
self.get_logger().info("Saved output.pcd")
rclpy.shutdown()
def main(args=None):
rclpy.init(args=args)
saver = PointCloudSaver()
rclpy.spin(saver)
if __name__ == '__main__':
main()

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import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2, PointField
import numpy as np
import struct
from sklearn.cluster import DBSCAN
class BasketballFrameDetector(Node):
def __init__(self):
super().__init__('basketball_frame_detector')
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.pointcloud_callback,
10
)
self.publisher = self.create_publisher(
PointCloud2,
'/basketball_frame_cloud',
10
)
self.pointcloud_buffer = []
def pointcloud_callback(self, msg):
points = self.pointcloud2_to_xyz(msg)
self.pointcloud_buffer.append(points)
self.get_logger().info(f'已保存点云组数: {len(self.pointcloud_buffer)}')
if len(self.pointcloud_buffer) < 10:
return
# 合并10组点云
all_points = np.vstack(self.pointcloud_buffer)
xy_points = all_points[:, :2]
self.get_logger().info(f'合并后点数: {xy_points.shape[0]}')
# 清空缓存,准备下一批
self.pointcloud_buffer = []
# 聚类识别
if len(xy_points) < 10:
return
clustering = DBSCAN(eps=0.3, min_samples=10).fit(xy_points)
labels = clustering.labels_
unique_labels = set(labels)
for label in unique_labels:
if label == -1:
continue
cluster = all_points[labels == label]
if len(cluster) < 30:
continue
min_x, min_y = np.min(cluster[:, :2], axis=0)
max_x, max_y = np.max(cluster[:, :2], axis=0)
width = abs(max_x - min_x)
height = abs(max_y - min_y)
self.get_logger().info(
f'聚类: label={label}, width={width:.2f}, height={height:.2f}, 点数={len(cluster)}'
)
if 1.6 < width < 2.0 and 0.8 < height < 1.2:
self.get_logger().info(
f'可能是篮球框: label={label}, width={width:.2f}, height={height:.2f}, 点数={len(cluster)}'
)
# 发布识别到的篮球框点云
cloud_msg = self.xyz_array_to_pointcloud2(cluster, msg.header)
self.publisher.publish(cloud_msg)
def pointcloud2_to_xyz(self, cloud_msg):
fmt = 'ffff' # x, y, z, intensity
points = []
for i in range(cloud_msg.width):
offset = i * cloud_msg.point_step
x, y, z, intensity = struct.unpack_from(fmt, cloud_msg.data, offset)
points.append([x, y, z, intensity])
return np.array(points)
def xyz_array_to_pointcloud2(self, points, header):
# 构造 PointCloud2 消息
msg = PointCloud2()
msg.header = header
msg.height = 1
msg.width = len(points)
msg.is_dense = True
msg.is_bigendian = False
msg.point_step = 16
msg.row_step = msg.point_step * msg.width
msg.fields = [
PointField(name='x', offset=0, datatype=7, count=1),
PointField(name='y', offset=4, datatype=7, count=1),
PointField(name='z', offset=8, datatype=7, count=1),
PointField(name='intensity', offset=12, datatype=7, count=1),
]
msg.data = b''.join([struct.pack('ffff', *p) for p in points])
return msg
def main(args=None):
rclpy.init(args=args)
node = BasketballFrameDetector()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
from sensor_msgs_py import point_cloud2
from geometry_msgs.msg import PoseStamped
import open3d as o3d
import numpy as np
from transforms3d.quaternions import mat2quat
class PointCloudLocalization(Node):
def __init__(self):
super().__init__('point_cloud_localizer')
# 加载参考点云地图 (PCD文件)
self.reference_map = o3d.io.read_point_cloud("/home/robofish/RC2025/lankuang.pcd") # 替换为你的PCD文件路径
if not self.reference_map.has_points():
self.get_logger().error("Failed to load reference map!")
rclpy.shutdown()
# 预处理参考地图
self.reference_map = self.reference_map.voxel_down_sample(voxel_size=0.05)
self.reference_map.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)[0]
# 创建ICP对象
self.icp = o3d.pipelines.registration.registration_icp
self.threshold = 0.5 # 匹配距离阈值 (米)
self.trans_init = np.identity(4) # 初始变换矩阵
# 订阅激光雷达点云
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.lidar_callback,
10)
# 发布估计位置
self.pose_pub = self.create_publisher(PoseStamped, '/estimated_pose', 10)
self.get_logger().info("Point Cloud Localization Node Initialized!")
def ros_pc2_to_o3d(self, ros_cloud):
"""将ROS PointCloud2转换为Open3D点云"""
# 提取xyz坐标
points = point_cloud2.read_points(ros_cloud, field_names=("x", "y", "z"), skip_nans=True)
xyz = np.array([ [p[0], p[1], p[2]] for p in points ], dtype=np.float32)
if xyz.shape[0] == 0:
return None
# 创建Open3D点云
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyz)
return pcd
def preprocess_pointcloud(self, pcd):
"""点云预处理"""
# 降采样
pcd = pcd.voxel_down_sample(voxel_size=0.03)
# 移除离群点
pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=1.0)
# 移除地面 (可选)
# plane_model, inliers = pcd.segment_plane(distance_threshold=0.1, ransac_n=3, num_iterations=100)
# pcd = pcd.select_by_index(inliers, invert=True)
return pcd
def lidar_callback(self, msg):
"""处理新的激光雷达数据"""
# 转换为Open3D格式
current_pcd = self.ros_pc2_to_o3d(msg)
if current_pcd is None:
self.get_logger().warn("Received empty point cloud!")
return
# 预处理当前点云
current_pcd = self.preprocess_pointcloud(current_pcd)
# 执行ICP配准
reg_result = self.icp(
current_pcd, self.reference_map, self.threshold,
self.trans_init,
o3d.pipelines.registration.TransformationEstimationPointToPoint(),
o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=50)
)
# 更新变换矩阵
self.trans_init = reg_result.transformation
# 提取位置和方向
translation = reg_result.transformation[:3, 3]
rotation_matrix = reg_result.transformation[:3, :3]
# 转换为四元数
quaternion = mat2quat(reg_result.transformation[:3, :3]) # 注意返回顺序为 [w, x, y, z]
# 发布位姿
pose_msg = PoseStamped()
pose_msg.header.stamp = self.get_clock().now().to_msg()
pose_msg.header.frame_id = "livox_frame"
pose_msg.pose.position.x = translation[0]
pose_msg.pose.position.y = translation[1]
pose_msg.pose.position.z = translation[2]
pose_msg.pose.orientation.x = quaternion[1] # x
pose_msg.pose.orientation.y = quaternion[2] # y
pose_msg.pose.orientation.z = quaternion[3] # z
pose_msg.pose.orientation.w = quaternion[0] # w
self.pose_pub.publish(pose_msg)
self.get_logger().info(f"Estimated Position: x={translation[0]:.2f}, y={translation[1]:.2f}, z={translation[2]:.2f}")
def main(args=None):
rclpy.init(args=args)
node = PointCloudLocalization()
try:
rclpy.spin(node)
except KeyboardInterrupt:
pass
finally:
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

75
src/rc_lidar/xiamian.py Normal file
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@ -0,0 +1,75 @@
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
from nav_msgs.msg import OccupancyGrid
import numpy as np
import struct
import time
class PointCloudToGrid(Node):
def __init__(self):
super().__init__('pointcloud_to_grid')
self.subscription = self.create_subscription(
PointCloud2,
'/livox/lidar_filtered',
self.pointcloud_callback,
10)
self.publisher = self.create_publisher(OccupancyGrid, '/lidar_grid', 10)
self.grid_size = 2000
self.resolution = 0.02
self.origin_x = -20.0
self.origin_y = -20.0
self.points_buffer = []
self.last_header = None
# 定时器每0.5秒触发一次
self.timer = self.create_timer(0.5, self.publish_grid)
def pointcloud_callback(self, msg):
points = self.pointcloud2_to_xyz_array(msg)
self.points_buffer.append(points)
self.last_header = msg.header # 保存最新header用于地图消息
def publish_grid(self):
if not self.points_buffer:
return
# 合并0.5秒内所有点
all_points = np.concatenate(self.points_buffer, axis=0)
grid = np.zeros((self.grid_size, self.grid_size), dtype=np.int8)
for x, y, z in all_points:
if z < 2.0:
ix = int((x - self.origin_x) / self.resolution)
iy = int((y - self.origin_y) / self.resolution)
if 0 <= ix < self.grid_size and 0 <= iy < self.grid_size:
grid[iy, ix] = 100
grid_msg = OccupancyGrid()
if self.last_header:
grid_msg.header = self.last_header
grid_msg.info.resolution = self.resolution
grid_msg.info.width = self.grid_size
grid_msg.info.height = self.grid_size
grid_msg.info.origin.position.x = self.origin_x
grid_msg.info.origin.position.y = self.origin_y
grid_msg.data = grid.flatten().tolist()
self.publisher.publish(grid_msg)
self.points_buffer.clear() # 清空缓存
def pointcloud2_to_xyz_array(self, cloud_msg):
# 解析 PointCloud2 数据为 numpy 数组
fmt = 'fff' # x, y, z
point_step = cloud_msg.point_step
data = cloud_msg.data
points = []
for i in range(0, len(data), point_step):
x, y, z = struct.unpack_from(fmt, data, i)
points.append([x, y, z])
return np.array(points)
def main(args=None):
rclpy.init(args=args)
node = PointCloudToGrid()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

51
src/rc_lidar/zhaoban.py Normal file
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@ -0,0 +1,51 @@
import numpy as np
from sklearn.cluster import DBSCAN
from scipy.optimize import leastsq
import matplotlib.pyplot as plt
def fit_circle(x, y):
# 拟合圆的函数
def calc_R(xc, yc):
return np.sqrt((x - xc)**2 + (y - yc)**2)
def f(c):
Ri = calc_R(*c)
return Ri - Ri.mean()
center_estimate = np.mean(x), np.mean(y)
center, _ = leastsq(f, center_estimate)
radius = calc_R(*center).mean()
return center[0], center[1], radius
def find_circle(points, eps=0.5, min_samples=10):
# 聚类
clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(points)
labels = clustering.labels_
# 只取最大簇
unique, counts = np.unique(labels[labels != -1], return_counts=True)
if len(unique) == 0:
raise ValueError("未找到有效聚类")
main_cluster = unique[np.argmax(counts)]
cluster_points = points[labels == main_cluster]
x, y = cluster_points[:, 0], cluster_points[:, 1]
# 拟合圆
xc, yc, r = fit_circle(x, y)
return xc, yc, r, cluster_points
if __name__ == "__main__":
# 示例数据
np.random.seed(0)
angle = np.linspace(0, 2 * np.pi, 100)
x = 5 + 3 * np.cos(angle) + np.random.normal(0, 0.1, 100)
y = 2 + 3 * np.sin(angle) + np.random.normal(0, 0.1, 100)
points = np.vstack((x, y)).T
xc, yc, r, cluster_points = find_circle(points)
print(f"圆心: ({xc:.2f}, {yc:.2f}), 半径: {r:.2f}")
# 可视化
plt.scatter(points[:, 0], points[:, 1], label='所有点')
plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label='聚类点')
circle = plt.Circle((xc, yc), r, color='r', fill=False, label='拟合圆')
plt.gca().add_patch(circle)
plt.legend()
plt.axis('equal')
plt.show()

0
src/rc_lidar/zhaoyuan.py Normal file
View File

View File

@ -1,10 +1,7 @@
devel/ @~!@
build/ build/
install/ install/
log/ log/
.vscode/ .vscode/
__pycache__/
.catkin_workspace
*.gv
*.pdf

View File

@ -327,3 +327,7 @@ Please add '/usr/local/lib' to the env LD_LIBRARY_PATH.
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib
source ~/.bashrc source ~/.bashrc
``` ```
KERNELS=="3-2:1.0", SUBSYSTEMS=="usb", MODE:="0666", SYMLINK+="underpan"
KERNELS=="3-1:1.0", SUBSYSTEMS=="usb", MODE:="0666", SYMLINK+="upper"
KERNELS=="3-2:1.0", SUBSYSTEMS=="usb", MODE:="0666", SYMLINK+="r2"

View File

@ -25,7 +25,7 @@
}, },
"lidar_configs" : [ "lidar_configs" : [
{ {
"ip" : "192.168.1.137", "ip" : "192.168.1.176",
"pcl_data_type" : 1, "pcl_data_type" : 1,
"pattern_mode" : 0, "pattern_mode" : 0,
"extrinsic_parameter" : { "extrinsic_parameter" : {

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@ -0,0 +1,121 @@
import rclpy
from rclpy.node import Node
import serial
import struct
from rm_msgs.msg import DataAim
class R2SerialNode(Node):
def __init__(self):
super().__init__('r2_serial_node')
# 声明参数
self.declare_parameter('yaw_port', '/dev/ttyUSB0')
self.declare_parameter('distance_port', '/dev/ttyUSB1')
self.declare_parameter('baud_rate', 115200)
# 获取参数
self.yaw_port = self.get_parameter('yaw_port').get_parameter_value().string_value
self.distance_port = self.get_parameter('distance_port').get_parameter_value().string_value
self.baud_rate = self.get_parameter('baud_rate').get_parameter_value().integer_value
# 创建定时器
# self.timer = self.create_timer(0.01, self.send_data) # 每100ms发送一次数据
# 初始化串口
try:
self.yaw_serial = serial.Serial(self.yaw_port, self.baud_rate, timeout=1)
self.get_logger().info(f"Yaw serial port {self.yaw_port} opened successfully")
except Exception as e:
self.get_logger().error(f"Failed to open yaw serial port: {e}")
self.yaw_serial = None
try:
self.distance_serial = serial.Serial(self.distance_port, self.baud_rate, timeout=1)
self.get_logger().info(f"Distance serial port {self.distance_port} opened successfully")
except Exception as e:
self.get_logger().error(f"Failed to open distance serial port: {e}")
self.distance_serial = None
# 数据存储
self.yaw1 = 0.0
self.yaw2 = 0.0
self.distance1 = 0.0
self.distance2 = 0.0
# 订阅话题
self.sub1 = self.create_subscription(
DataAim,
'/chassis/data_aim',
self.data_aim_callback,
10
)
self.sub2 = self.create_subscription(
DataAim,
'/data_aim_r2',
self.data_aim_r2_callback,
10
)
self.get_logger().info("R2 Serial Node initialized")
def data_aim_callback(self, msg):
"""处理/data_aim话题的回调"""
self.yaw1 = msg.yaw
self.distance1 = msg.distance
self.get_logger().debug(f"Received data_aim: yaw={self.yaw1}, distance={self.distance1}")
self.send_data()
def data_aim_r2_callback(self, msg):
"""处理/data_aim_r2话题的回调"""
self.yaw2 = msg.yaw
self.distance2 = msg.distance
self.get_logger().debug(f"Received data_aim_r2: yaw={self.yaw2}, distance={self.distance2}")
self.send_data()
def send_data(self):
"""发送数据到串口"""
# 打包所有数据包头FF + yaw1 + distance1 + yaw2 + distance2 + 包尾FE
try:
# 包头FF + 四个float32值 + 包尾FE
all_data = struct.pack('<ffff', self.yaw1, self.distance1, self.yaw2, self.distance2) # 小端序
data_packet = b'\xFF' + all_data + b'\xFE'
# 通过yaw串口发送
if self.yaw_serial and self.yaw_serial.is_open:
self.yaw_serial.write(data_packet)
print(f"Sent yaw data: {self.yaw1}, {self.yaw2}")
# 通过distance串口发送相同数据
if self.distance_serial and self.distance_serial.is_open:
self.distance_serial.write(data_packet)
print(f"Sent distance data: {self.distance1}, {self.distance2}")
except Exception as e:
self.get_logger().error(f"Failed to send data: {e}")
def __del__(self):
"""析构函数,关闭串口"""
if hasattr(self, 'yaw_serial') and self.yaw_serial and self.yaw_serial.is_open:
self.yaw_serial.close()
self.get_logger().info("Yaw serial port closed")
if hasattr(self, 'distance_serial') and self.distance_serial and self.distance_serial.is_open:
self.distance_serial.close()
self.get_logger().info("Distance serial port closed")
def main(args=None):
rclpy.init(args=args)
try:
node = R2SerialNode()
rclpy.spin(node)
except KeyboardInterrupt:
pass
except Exception as e:
print(f"Error in main: {e}")
finally:
if 'node' in locals():
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -1,79 +0,0 @@
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
import serial
import struct
from rm_msgs.msg import DataAim # 假设使用DataAim消息类型您可以根据实际消息类型调整
class AimDataSerial(Node):
def __init__(self):
super().__init__('aim_data_serial')
# 串口配置
self.serial_port = '/dev/ttyUSB0' # 根据实际串口设备调整
self.baud_rate = 115200
try:
self.serial_conn = serial.Serial(
port=self.serial_port,
baudrate=self.baud_rate,
timeout=1
)
self.get_logger().info(f'Serial port {self.serial_port} opened successfully')
except Exception as e:
self.get_logger().error(f'Failed to open serial port: {e}')
return
# 订阅话题
self.subscription = self.create_subscription(
DataAim, # 根据实际消息类型调整
'/chassis/data_aim',
self.aim_callback,
10
)
self.get_logger().info('Aim data serial node started')
def aim_callback(self, msg):
try:
# 提取yaw和distance数据
# 根据实际消息结构调整这里假设使用Point32的x和y字段
yaw = msg.yaw
distance = msg.distance
self.get_logger().info(f'Received - yaw: {yaw}, distance: {distance}')
# 构建发送数据包
# 格式: 包头(0xFF) + yaw(4字节float) + distance(4字节float) + 包尾(0xFE)
packet = bytearray()
packet.append(0xFF) # 包头
packet.extend(struct.pack('<f', yaw)) # yaw (小端序float)
packet.extend(struct.pack('<f', distance)) # distance (小端序float)
packet.append(0xFE) # 包尾
# 发送数据
self.serial_conn.write(packet)
self.get_logger().debug(f'Sent packet: {packet.hex()}')
except Exception as e:
self.get_logger().error(f'Error in aim_callback: {e}')
def __del__(self):
if hasattr(self, 'serial_conn') and self.serial_conn.is_open:
self.serial_conn.close()
self.get_logger().info('Serial port closed')
def main(args=None):
rclpy.init(args=args)
try:
node = AimDataSerial()
rclpy.spin(node)
except KeyboardInterrupt:
pass
finally:
if rclpy.ok():
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -1,79 +0,0 @@
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
import serial
import struct
from rm_msgs.msg import DataAim # 假设使用DataAim消息类型您可以根据实际消息类型调整
class AimDataSerial(Node):
def __init__(self):
super().__init__('aim_data_serial')
# 串口配置
self.serial_port = '/dev/ttyUSB0' # 根据实际串口设备调整
self.baud_rate = 115200
try:
self.serial_conn = serial.Serial(
port=self.serial_port,
baudrate=self.baud_rate,
timeout=1
)
self.get_logger().info(f'Serial port {self.serial_port} opened successfully')
except Exception as e:
self.get_logger().error(f'Failed to open serial port: {e}')
return
# 订阅话题
self.subscription = self.create_subscription(
DataAim, # 根据实际消息类型调整
'/chassis/data_aim',
self.aim_callback,
10
)
self.get_logger().info('Aim data serial node started')
def aim_callback(self, msg):
try:
# 提取yaw和distance数据
# 根据实际消息结构调整这里假设使用Point32的x和y字段
yaw = msg.yaw
distance = msg.distance
self.get_logger().info(f'Received - yaw: {yaw}, distance: {distance}')
# 构建发送数据包
# 格式: 包头(0xFF) + yaw(4字节float) + distance(4字节float) + 包尾(0xFE)
packet = bytearray()
packet.append(0xFF) # 包头
packet.extend(struct.pack('<f', yaw)) # yaw (小端序float)
packet.extend(struct.pack('<f', distance)) # distance (小端序float)
packet.append(0xFE) # 包尾
# 发送数据
self.serial_conn.write(packet)
self.get_logger().debug(f'Sent packet: {packet.hex()}')
except Exception as e:
self.get_logger().error(f'Error in aim_callback: {e}')
def __del__(self):
if hasattr(self, 'serial_conn') and self.serial_conn.is_open:
self.serial_conn.close()
self.get_logger().info('Serial port closed')
def main(args=None):
rclpy.init(args=args)
try:
node = AimDataSerial()
rclpy.spin(node)
except KeyboardInterrupt:
pass
finally:
if rclpy.ok():
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -0,0 +1,36 @@
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import PointStamped
from rm_msgs.msg import MoveGoal
class ClickedGoalPublisher(Node):
def __init__(self):
super().__init__('clicked_goal_publisher')
self.subscription = self.create_subscription(
PointStamped,
'/clicked_point',
self.clicked_callback,
10
)
self.publisher = self.create_publisher(MoveGoal, '/move_goal', 10)
def clicked_callback(self, msg):
goal = MoveGoal()
goal.x = msg.point.x
goal.y = msg.point.y
goal.angle = 0.0
goal.max_speed = 0.0
goal.tolerance = 0.1
goal.rotor = False
self.publisher.publish(goal)
self.get_logger().info(f'发布目标: x={goal.x:.2f}, y={goal.y:.2f}')
def main(args=None):
rclpy.init(args=args)
node = ClickedGoalPublisher()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -1,101 +0,0 @@
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
import serial
import struct
import tf2_ros
from geometry_msgs.msg import TransformStamped
from tf2_geometry_msgs import do_transform_pose
from geometry_msgs.msg import PoseStamped
import math
class SelfPositionSerial(Node):
def __init__(self):
super().__init__('self_position_serial')
# 串口配置
self.serial_port = '/dev/ttyUSB1' # 根据实际串口设备调整
self.baud_rate = 115200
try:
self.serial_conn = serial.Serial(
port=self.serial_port,
baudrate=self.baud_rate,
timeout=1
)
self.get_logger().info(f'Serial port {self.serial_port} opened successfully')
except Exception as e:
self.get_logger().error(f'Failed to open serial port: {e}')
return
# TF监听器
self.tf_buffer = tf2_ros.Buffer()
self.tf_listener = tf2_ros.TransformListener(self.tf_buffer, self)
# 定时器,定期发送位置信息
self.timer = self.create_timer(0.1, self.send_position) # 10Hz
self.get_logger().info('Self position serial node started')
def send_position(self):
try:
# 获取base_link在map下的位置
transform = self.tf_buffer.lookup_transform(
'map', 'base_link', rclpy.time.Time())
# 提取位置和朝向
x = transform.transform.translation.x
y = transform.transform.translation.y
z = transform.transform.translation.z
# 计算yaw角从四元数
quat = transform.transform.rotation
yaw = math.atan2(2.0 * (quat.w * quat.z + quat.x * quat.y),
1.0 - 2.0 * (quat.y * quat.y + quat.z * quat.z))
self.get_logger().debug(f'Position - x: {x:.3f}, y: {y:.3f}, z: {z:.3f}, yaw: {yaw:.3f}')
# 构建发送数据包
# 格式: 包头(0xFF) + x(4字节) + y(4字节) + z(4字节) + yaw(4字节) + 校验和(1字节) + 包尾(0xFE)
packet = bytearray()
packet.append(0xFF) # 包头
packet.extend(struct.pack('<f', x)) # x坐标
packet.extend(struct.pack('<f', y)) # y坐标
packet.extend(struct.pack('<f', z)) # z坐标
packet.extend(struct.pack('<f', yaw)) # yaw角
# 计算校验和(数据部分的异或校验)
checksum = 0
for i in range(1, len(packet)):
checksum ^= packet[i]
packet.append(checksum)
packet.append(0xFE) # 包尾
# 发送数据
self.serial_conn.write(packet)
self.get_logger().debug(f'Sent position packet: {packet.hex()}')
except Exception as e:
self.get_logger().warn(f'Failed to get transform or send data: {e}')
def __del__(self):
if hasattr(self, 'serial_conn') and self.serial_conn.is_open:
self.serial_conn.close()
self.get_logger().info('Serial port closed')
def main(args=None):
rclpy.init(args=args)
try:
node = SelfPositionSerial()
rclpy.spin(node)
except KeyboardInterrupt:
pass
finally:
if rclpy.ok():
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -0,0 +1,169 @@
import serial
import struct
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import PointStamped, TransformStamped
from rm_msgs.msg import DataAim
import tf2_ros
import tf2_geometry_msgs
import math
# 串口参数
SERIAL_PORT = '/dev/ttyACM0' # 根据实际情况修改
BAUDRATE = 115200
# 数据包格式
PACKET_FORMAT = '<BfffB' # 1字节int8头3个float1字节int8尾
PACKET_SIZE = struct.calcsize(PACKET_FORMAT)
HEADER = 0xAA
TAIL = 0xBB
class SerialReceiver(Node):
def __init__(self):
super().__init__('serial_receiver')
# 创建发布者
self.point_publisher = self.create_publisher(PointStamped, 'r2_map_point', 10)
self.data_aim_publisher = self.create_publisher(DataAim, 'data_aim_r2', 10)
# 创建TF监听器和广播器
self.tf_buffer = tf2_ros.Buffer()
self.tf_listener = tf2_ros.TransformListener(self.tf_buffer, self)
self.tf_broadcaster = tf2_ros.TransformBroadcaster(self)
# 串口初始化
self.ser = serial.Serial(SERIAL_PORT, BAUDRATE, timeout=1)
self.get_logger().info(f"打开串口 {SERIAL_PORT},波特率 {BAUDRATE}")
# 创建定时器用于读取串口数据
self.timer = self.create_timer(0.001, self.read_serial_data) # 10ms
self.buffer = bytearray()
def read_serial_data(self):
try:
data = self.ser.read(1)
if data:
self.buffer += data
# 保证缓冲区长度不超过一个包
if len(self.buffer) > PACKET_SIZE:
self.buffer = self.buffer[-PACKET_SIZE:]
# 检查是否有完整包
if len(self.buffer) >= PACKET_SIZE:
# 检查包头和包尾
if self.buffer[0] == HEADER and self.buffer[PACKET_SIZE-1] == TAIL:
# 解析数据
_, x, y, z, _ = struct.unpack(PACKET_FORMAT, self.buffer[:PACKET_SIZE])
self.get_logger().info(f"接收到数据: x={x:.3f}, y={y:.3f}, z={z:.3f}")
# 发布map坐标点
self.publish_map_point(x, y, z)
# 发布R2的TF变换
self.publish_r2_tf(x, y, z)
# 计算相对于base_link的角度和距离
self.calculate_and_publish_data_aim(x, y, z)
self.buffer = self.buffer[PACKET_SIZE:] # 移除已处理数据
else:
# 移除第一个字节,继续查找包头
self.buffer = self.buffer[1:]
except Exception as e:
self.get_logger().error(f"读取串口数据错误: {e}")
def publish_map_point(self, x, y, z):
"""发布地图坐标点"""
point_msg = PointStamped()
point_msg.header.frame_id = 'map'
point_msg.header.stamp = self.get_clock().now().to_msg()
point_msg.point.x = x
point_msg.point.y = y
point_msg.point.z = z
self.point_publisher.publish(point_msg)
def publish_r2_tf(self, x, y, z):
"""发布R2的TF变换"""
transform = TransformStamped()
# 设置header
transform.header.stamp = self.get_clock().now().to_msg()
transform.header.frame_id = 'map'
transform.child_frame_id = 'r2_robot'
# 设置位置
transform.transform.translation.x = x
transform.transform.translation.y = y
transform.transform.translation.z = z
# 设置旋转假设R2面向x轴正方向无旋转
transform.transform.rotation.x = 0.0
transform.transform.rotation.y = 0.0
transform.transform.rotation.z = 0.0
transform.transform.rotation.w = 1.0
# 发布TF变换
self.tf_broadcaster.sendTransform(transform)
def calculate_and_publish_data_aim(self, x, y, z):
"""计算相对于base_link的角度和距离并发布DataAim消息"""
try:
# 创建map坐标系下的点
point_stamped = PointStamped()
point_stamped.header.frame_id = 'map'
point_stamped.header.stamp = self.get_clock().now().to_msg()
point_stamped.point.x = x
point_stamped.point.y = y
point_stamped.point.z = z
# 获取从map到base_link的变换
transform = self.tf_buffer.lookup_transform(
'base_link', 'map', rclpy.time.Time())
# 将点变换到base_link坐标系
point_base_link = tf2_geometry_msgs.do_transform_point(point_stamped, transform)
# 计算距离
distance = math.sqrt(
point_base_link.point.x**2 +
point_base_link.point.y**2 +
point_base_link.point.z**2
)
# 计算yaw角度绕z轴旋转角度
yaw = math.atan2(point_base_link.point.y, point_base_link.point.x)
# 发布DataAim消息
data_aim_msg = DataAim()
data_aim_msg.yaw = yaw
data_aim_msg.distance = distance
self.data_aim_publisher.publish(data_aim_msg)
self.get_logger().info(f"发布DataAim: yaw={yaw:.3f} rad, distance={distance:.3f} m")
except (tf2_ros.LookupException, tf2_ros.ConnectivityException, tf2_ros.ExtrapolationException) as e:
self.get_logger().warn(f"TF变换失败: {e}")
def destroy_node(self):
"""节点销毁时关闭串口"""
if hasattr(self, 'ser') and self.ser.is_open:
self.ser.close()
super().destroy_node()
def main(args=None):
rclpy.init(args=args)
receiver = SerialReceiver()
try:
rclpy.spin(receiver)
except KeyboardInterrupt:
print("接收中断,退出程序。")
finally:
receiver.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -1,241 +0,0 @@
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
import serial
import struct
import tf2_ros
from geometry_msgs.msg import TransformStamped
from rm_msgs.msg import DataAim
import math
import threading
from rclpy.executors import MultiThreadedExecutor
class ReceiveAndPubNode(Node):
def __init__(self):
super().__init__('receive_and_pub')
# 串口配置
self.receive_port = '/dev/ttyUSB2' # 接收串口
self.send_port = '/dev/ttyUSB3' # 发送串口
self.baud_rate = 115200
try:
# 接收串口
self.receive_serial = serial.Serial(
port=self.receive_port,
baudrate=self.baud_rate,
timeout=0.1
)
# 发送串口
self.send_serial = serial.Serial(
port=self.send_port,
baudrate=self.baud_rate,
timeout=1
)
self.get_logger().info(f'Serial ports opened: {self.receive_port}, {self.send_port}')
except Exception as e:
self.get_logger().error(f'Failed to open serial ports: {e}')
return
# TF广播器和监听器
self.tf_broadcaster = tf2_ros.TransformBroadcaster(self)
self.tf_buffer = tf2_ros.Buffer()
self.tf_listener = tf2_ros.TransformListener(self.tf_buffer, self)
# 订阅瞄准数据
self.subscription = self.create_subscription(
DataAim,
'/chassis/data_aim',
self.aim_callback,
10
)
# 存储接收到的位置信息和瞄准数据
self.received_position = {'x': 0.0, 'y': 0.0, 'z': 0.0, 'yaw': 0.0, 'valid': False}
self.aim_data = {'yaw': 0.0, 'distance': 0.0, 'valid': False}
# 启动接收线程
self.receive_thread = threading.Thread(target=self.receive_position_thread)
self.receive_thread.daemon = True
self.receive_thread.start()
# 定时发送数据
self.send_timer = self.create_timer(0.1, self.send_target_data) # 10Hz
self.get_logger().info('Receive and pub node started')
def receive_position_thread(self):
"""接收位置信息的线程"""
buffer = bytearray()
while rclpy.ok():
try:
if self.receive_serial.in_waiting > 0:
data = self.receive_serial.read(self.receive_serial.in_waiting)
buffer.extend(data)
# 查找完整的数据包
while len(buffer) >= 22: # 最小包长度:包头(1) + 数据(16) + 校验(1) + 包尾(1) = 19
# 查找包头
start_idx = buffer.find(0xFF)
if start_idx == -1:
break
# 移除包头前的数据
if start_idx > 0:
buffer = buffer[start_idx:]
# 检查包长度
if len(buffer) < 22:
break
# 查找包尾
if buffer[21] == 0xFE:
# 校验数据
checksum = 0
for i in range(1, 20):
checksum ^= buffer[i]
if checksum == buffer[20]:
# 解析数据
x = struct.unpack('<f', buffer[1:5])[0]
y = struct.unpack('<f', buffer[5:9])[0]
z = struct.unpack('<f', buffer[9:13])[0]
yaw = struct.unpack('<f', buffer[13:17])[0]
self.received_position = {
'x': x, 'y': y, 'z': z, 'yaw': yaw, 'valid': True
}
# 发布tf变换
self.publish_r2_transform(x, y, z, yaw)
self.get_logger().debug(f'Received position: x={x:.3f}, y={y:.3f}, z={z:.3f}, yaw={yaw:.3f}')
else:
self.get_logger().warn('Checksum error')
# 移除已处理的数据包
buffer = buffer[22:]
else:
# 包尾不匹配,移除包头继续查找
buffer = buffer[1:]
except Exception as e:
self.get_logger().error(f'Error in receive thread: {e}')
def publish_r2_transform(self, x, y, z, yaw):
"""发布R2到map的tf变换"""
try:
t = TransformStamped()
t.header.stamp = self.get_clock().now().to_msg()
t.header.frame_id = 'map'
t.child_frame_id = 'R2'
t.transform.translation.x = x
t.transform.translation.y = y
t.transform.translation.z = z
# 将yaw角转换为四元数
t.transform.rotation.x = 0.0
t.transform.rotation.y = 0.0
t.transform.rotation.z = math.sin(yaw / 2.0)
t.transform.rotation.w = math.cos(yaw / 2.0)
self.tf_broadcaster.sendTransform(t)
except Exception as e:
self.get_logger().error(f'Error publishing tf: {e}')
def aim_callback(self, msg):
"""接收瞄准数据"""
try:
self.aim_data = {
'yaw': msg.yaw,
'distance': msg.distance,
'valid': True
}
self.get_logger().debug(f'Received aim data: yaw={msg.yaw:.3f}, distance={msg.distance:.3f}')
except Exception as e:
self.get_logger().error(f'Error in aim callback: {e}')
def get_r2_target_data(self):
"""获取R2的瞄准数据"""
try:
if not self.received_position['valid']:
return 0.0, 0.0
# 这里可以根据R2的位置计算目标数据
# 简化处理返回R2的yaw角和到原点的距离
r2_yaw = self.received_position['yaw']
r2_distance = math.sqrt(
self.received_position['x']**2 +
self.received_position['y']**2
)
return r2_yaw, r2_distance
except Exception as e:
self.get_logger().error(f'Error getting R2 target data: {e}')
return 0.0, 0.0
def send_target_data(self):
"""发送目标数据"""
try:
# 获取原始目标数据
if self.aim_data['valid']:
original_yaw = self.aim_data['yaw']
original_distance = self.aim_data['distance']
else:
original_yaw = 0.0
original_distance = 0.0
# 获取R2目标数据
r2_yaw, r2_distance = self.get_r2_target_data()
# 构建发送数据包
# 格式: 包头(0xFF) + original_yaw(4字节) + original_distance(4字节) + r2_yaw(4字节) + r2_distance(4字节) + 校验和(1字节) + 包尾(0xFE)
packet = bytearray()
packet.append(0xFF) # 包头
packet.extend(struct.pack('<f', original_yaw)) # 原始yaw
packet.extend(struct.pack('<f', original_distance)) # 原始distance
packet.extend(struct.pack('<f', r2_yaw)) # R2 yaw
packet.extend(struct.pack('<f', r2_distance)) # R2 distance
# 计算校验和
checksum = 0
for i in range(1, len(packet)):
checksum ^= packet[i]
packet.append(checksum)
packet.append(0xFE) # 包尾
# 发送数据
self.send_serial.write(packet)
self.get_logger().debug(f'Sent target packet: original({original_yaw:.3f},{original_distance:.3f}) R2({r2_yaw:.3f},{r2_distance:.3f})')
except Exception as e:
self.get_logger().error(f'Error sending target data: {e}')
def __del__(self):
if hasattr(self, 'receive_serial') and self.receive_serial.is_open:
self.receive_serial.close()
if hasattr(self, 'send_serial') and self.send_serial.is_open:
self.send_serial.close()
self.get_logger().info('Serial ports closed')
def main(args=None):
rclpy.init(args=args)
try:
node = ReceiveAndPubNode()
executor = MultiThreadedExecutor()
executor.add_node(node)
executor.spin()
except KeyboardInterrupt:
pass
finally:
if rclpy.ok():
rclpy.shutdown()
if __name__ == '__main__':
main()

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@ -50,7 +50,7 @@ private:
initial_pose_sub_; initial_pose_sub_;
rclcpp::Subscription<sensor_msgs::msg::PointCloud2>::SharedPtr rclcpp::Subscription<sensor_msgs::msg::PointCloud2>::SharedPtr
pointcloud_sub_; pointcloud_sub_;
// rclcpp::TimerBase::SharedPtr timer_; rclcpp::TimerBase::SharedPtr timer_;
std::shared_ptr<tf2_ros::TransformBroadcaster> tf_broadcaster_; std::shared_ptr<tf2_ros::TransformBroadcaster> tf_broadcaster_;
std::shared_ptr<tf2_ros::Buffer> tf_buffer_; std::shared_ptr<tf2_ros::Buffer> tf_buffer_;
std::shared_ptr<tf2_ros::TransformListener> tf_listener_; std::shared_ptr<tf2_ros::TransformListener> tf_listener_;

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@ -4,3 +4,6 @@ Some ROS 2 custom messages for Robotaster
Usage Usage
Modify or add files in the /msg directory as needed Modify or add files in the /msg directory as needed
colcon build colcon build
KERNELS=="3-2:1.0", SUBSYSTEMS=="USB", MODE:="0666",SYMLINK+="underpan"
KERNELS=="3-1:1.0", SUBSYSTEMS=="USB", MODE:="0666",SYMLINK+="upper"
KERNELS=="3-3:1.0", SUBSYSTEMS=="USB", MODE:="0666",SYMLINK+="r2"

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@ -0,0 +1,7 @@
image: rc_map1.pgm
mode: trinary
resolution: 0.05
origin: [-6.19, -7.3, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

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image: rc_map1.pgm
mode: trinary
resolution: 0.05
origin: [-7.9, -8.67, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

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src/rm_nav_bringup/PCD/map1.pcd Executable file

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src/rm_nav_bringup/PCD/map2.pcd Executable file

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@ -25,7 +25,7 @@
}, },
"lidar_configs" : [ "lidar_configs" : [
{ {
"ip" : "192.168.1.176", "ip" : "192.168.1.137",
"pcl_data_type" : 1, "pcl_data_type" : 1,
"pattern_mode" : 0, "pattern_mode" : 0,
"extrinsic_parameter" : { "extrinsic_parameter" : {

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@ -7,7 +7,7 @@
filter_size_map: 0.5 filter_size_map: 0.5
cube_side_length: 1000.0 cube_side_length: 1000.0
runtime_pos_log_enable: false runtime_pos_log_enable: false
map_file_path: "src/rm_nav_bringup/PCD/RC2025.pcd" map_file_path: "src/rm_nav_bringup/PCD/test2.pcd"
common: common:
lid_topic: "/livox/lidar" lid_topic: "/livox/lidar"

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@ -3,7 +3,7 @@
use_sim_time: false use_sim_time: false
rough_leaf_size: 0.4 rough_leaf_size: 0.4
refine_leaf_size: 0.1 refine_leaf_size: 0.1
pcd_path: "" pcd_path: "map2"
map_frame_id: "map" map_frame_id: "map"
odom_frame_id: "odom" odom_frame_id: "odom"
range_odom_frame_id: "lidar_odom" range_odom_frame_id: "lidar_odom"

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@ -1,3 +1,3 @@
base_link2livox_frame: base_link2livox_frame:
xyz: "\"0.037 -0.354 0.41\"" xyz: "\"0.251 -0.1285 0.397\""
rpy: "\"0.0 0.0 0.0\"" rpy: "\"0.0 0.0 0.0\""

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src/rm_nav_bringup/map/map1.data Executable file

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src/rm_nav_bringup/map/map1.pgm Executable file

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@ -0,0 +1,7 @@
image: map1.pgm
mode: trinary
resolution: 0.05
origin: [-9.04, -13.8, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

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src/rm_nav_bringup/map/map2.data Executable file

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src/rm_nav_bringup/map/map2.pgm Executable file

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@ -0,0 +1,7 @@
image: map2.pgm
mode: trinary
resolution: 0.05
origin: [-7.08, -15.4, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

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@ -0,0 +1,7 @@
image: rc_map1.pgm
mode: trinary
resolution: 0.05
origin: [-7.9, -8.67, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

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@ -0,0 +1,7 @@
image: test.pgm
mode: trinary
resolution: 0.05
origin: [-9.04, -13.8, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

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@ -0,0 +1,7 @@
image: test2.pgm
mode: trinary
resolution: 0.05
origin: [-5.88, -6.46, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25