lidar_cluster ROS 2 package

LIDAR pointcloud clustering in ROS 2 Humble

Build this ROS 2 package

In 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

ROS 2 graph

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

Cluster nodes

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

Remarks

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

Images

[!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

Citation

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}}