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

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

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

Don't forget to source before ROS commands.

source ~/ros2_ws/install/setup.bash

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