lidar_cluster
ROS 2 packageLIDAR pointcloud clustering in ROS 2
Humble
ROS 2
packageIn the following ~/ros2_ws
is assumed as the ROS 2 workspace:
cd ~/ros2_ws/src
git clone https://github.com/jkk-research/lidar_cluster_ros2
cd ~/ros2_ws
Build either with:
colcon build --packages-select lidar_cluster --symlink-install
or with optimized build:
MAKEFLAGS="-j4" colcon build --packages-select lidar_cluster --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release
[!TIP] There is a detailded decription how to make this package work with ground segmentation: jkk-research.github.io/workshops/clustering_a
graph LR;
p[ /input_points<br/>sensor_msgs::PointCloud2]:::white --> cluster([ /cluster_node]):::light
tf[ /tf <br/>optional topic]:::dash -.-> cluster
cluster --> f1[ /clustered_points<br/>sensor_msgs::PointCloud2]:::white
cluster --> f2[ /clustered_marker<br/>visualization_msgs::MarkerArray]:::white
classDef light fill:#34aec5,stroke:#152742,stroke-width:2px,color:#152742
classDef dark fill:#152742,stroke:#34aec5,stroke-width:2px,color:#34aec5
classDef white fill:#ffffff,stroke:#152742,stroke-width:2px,color:#15274
classDef dash fill:#ffffff,stroke:#152742,stroke-width:2px,color:#15274, stroke-dasharray: 5 5
classDef red fill:#ef4638,stroke:#152742,stroke-width:2px,color:#fff
Node | Clustering | Implementation | Description | Additional Resources |
---|---|---|---|---|
dbscan_spatial |
DBSCAN | spatial | DBSCAN non-grid implementation | |
dbscan_grid |
DBSAN | grid | DBSCAN voxel-grid-based implementation | |
dblane_spatial |
DBlane | spatial | DBlane non-grid implementation | flowchart, notebooks |
dblane_f1s |
DBlane | formula | DBlane formula 1 student implementation | notebooks |
euclidean_spatial |
Euclidean | spatial | PCL implementation of Euclidean clustering non-grid implementation | PCL docs |
euclidean_grid |
Euclidean | grid | PCL implementation of Euclidean clustering voxel-grid-based implementation | PCL docs |
ros2 run lidar_cluster dblane_spatial
ros2 launch lidar_cluster dblane_spatial.launch.py
ros2 launch lidar_cluster dblane_f1s.launch.py topic:=/input_points
In VS code it is advised to add the following to include path:
${workspaceFolder}/**
/opt/ros/humble/include/**
/usr/include/pcl-1.12/**
/usr/include/eigen3/**
If you are not sure where your header files are use e.g.:
find /usr/include -name point_cloud.h
find /usr/include -name crop_box.h
[!NOTE]
The following images shows two possible usage of clustering: an urban scenario and a race scenario. The first row shows a camera image that corresponds to the LIDAR image in the second row. In the third row an example is shown of the clustered LIDAR pointcloud with yellow.
Urban scenario | Race scenario |
---|---|
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If you use any of this code please consider citing the paper:
@INPROCEEDINGS{10607072,
author={Unger, Miklós and Horváth, Ernő and Pup, Dániel and Pozna, Claudiu Radu},
booktitle={2024 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)},
title={Towards Robust LIDAR Lane Clustering for Autonomous Vehicle Perception in ROS 2},
year={2024},
pages={229-234},
keywords={Laser radar;Lane detection;Source coding;Clustering algorithms;Robustness;Pattern recognition;Autonomous vehicles;self-driving;autonomous;point cloud;LIDAR;proceeding;filter;geometric patterns},
doi={10.1109/MOST60774.2024.00031}}