2026-06-27 · 14 min

Integrating a LiDAR in ROS2: Complete Guide

Step-by-step guide to integrate an Ouster, Hesai, Livox or SICK LiDAR into ROS2 (Humble/Iron/Jazzy): driver installation, network setup, PTP, QoS, launch files, RViz2 visualization, SLAM and sensor fusion.

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1. Introduction

LiDAR has become essential in mobile robotics for autonomous navigation, SLAM mapping and perception. This guide covers integration of Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) and SICK (sick_scan_xd) drivers in ROS2 Humble, Iron and Jazzy.

2. Driver Installation

Ouster: git clone --branch ros2 https://github.com/ouster-lidar/ouster-ros.git, colcon build. Topics: /ouster/points (PointCloud2), /ouster/imu. Hesai: git clone https://github.com/HesaiTechnology/hesai_ros_driver.git. Topics: /hesai/pandar. Livox: git clone https://github.com/Livox-SDK/livox_ros_driver2.git. Topics: /livox/lidar. SICK: git clone https://github.com/SICKAG/sick_scan_xd.git. Topics: /scan (2D), /cloud (3D).

3. Network Configuration

Set a static IP on the same subnet as the LiDAR (e.g. 192.168.1.50/24). Enable PTP (IEEE 1588) via linuxptp for precise timestamping: sudo ptp4l -i eth0 -m -s. Use BEST_EFFORT QoS in ROS2 for point clouds to prevent DDS saturation.

4. Visualization and Advanced Processing

Visualize the point cloud in RViz2 (Add > PointCloud2). For 2D SLAM, use slam_toolbox after projection via pointcloud_to_laserscan. For 3D SLAM, use Cartographer. LiDAR/IMU fusion uses robot_localization. For clustering and obstacle detection, use PCL (pcl_ros).

5. Checklist and Conclusion

Verify: ROS2 sourced, driver built, static IP, ping OK, PTP active, BEST_EFFORT QoS, RViz2 working, TF published and SLAM validated. Key to successful integration: network config, ROS2 QoS and correct TF tree.

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