[{"heading":{"fr":"1. Introduction","en":"1. Introduction","de":"1. Introduction","it":"1. Introduzione","pt":"1. Introdução","es":"1. Introducción","be":"1. Introduction","ch":"1. Einleitung","lu":"1. Aféierung"},"body":[{"fr":"Le LiDAR est devenu un capteur incontournable en robotique mobile pour la navigation autonome, la cartographie SLAM et la perception. Ce guide couvre l'intégration des drivers Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) et SICK (sick_scan_xd) dans ROS2 Humble, Iron et Jazzy.","en":"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.","de":"LiDAR ist in der mobilen Robotik für autonome Navigation, SLAM-Kartierung und Wahrnehmung unverzichtbar geworden. Dieser Leitfaden behandelt die Integration der Treiber von Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) und SICK (sick_scan_xd) in ROS2 Humble, Iron und Jazzy.","it":"Il LiDAR è diventato un sensore imprescindibile nella robotica mobile per la navigazione autonoma, la mappatura SLAM e la percezione. Questa guida copre l\'integrazione dei driver Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) e SICK (sick_scan_xd) in ROS2 Humble, Iron e Jazzy.","pt":"O LiDAR tornou-se essencial em robótica móvel para navegação autónoma, cartografia SLAM e perceção. Este guia cobre a integração dos drivers Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) e SICK (sick_scan_xd) no ROS2 Humble, Iron e Jazzy.","es":"El LiDAR se ha convertido en un sensor imprescindible en robótica móvil para la navegación autónoma, la cartografía SLAM y la percepción. Esta guía cubre la integración de los controladores Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) y SICK (sick_scan_xd) en ROS2 Humble, Iron y Jazzy.","be":"Le LiDAR est devenu un capteur incontournable en robotique mobile pour la navigation autonome, la cartographie SLAM et la perception. Ce guide couvre l\'intégration des drivers Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) et SICK (sick_scan_xd) dans ROS2 Humble, Iron et Jazzy.","ch":"LiDAR ist zu einem unverzichtbaren Sensor in der mobilen Robotik für autonome Navigation, SLAM-Kartierung und Wahrnehmung geworden. Dieser Leitfaden behandelt die Integration der Treiber Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) und SICK (sick_scan_xd) in ROS2 Humble, Iron und Jazzy.","lu":"LiDAR ist in der mobilen Robotik für autonome Navigation, SLAM-Kartierung an Wahrnehmung unverzichtbar geworden. Dieser Leitfaden behandelt die Integration der Treiber von Ouster (ouster-ros), Hesai (hesai_ros_driver), Livox (livox_ros_driver2) an SICK (sick_scan_xd) in ROS2 Humble, Iron an Jazzy."}]},{"heading":{"fr":"2. Installation des drivers","en":"2. Driver Installation","de":"2. Installation des drivers","it":"2. Installazione dei driver","pt":"2. Instalação dos drivers","es":"2. Instalación de los controladores","be":"2. Installation des drivers","ch":"2. Treiberinstallation","lu":"2. Treiberinstallatioun"},"body":[{"fr":"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).","en":"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).","de":"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).","it":"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).","pt":"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).","es":"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).","be":"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).","ch":"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).","lu":"Ouster: git clone --branch ros2 https://github.com/ouster-lidar/ouster-ros.git, colcon build. Themen: /ouster/points (PointCloud2), /ouster/imu. Hesai: git clone https://github.com/HesaiTechnology/hesai_ros_driver.git. Themen: /hesai/pandar. Livox: git clone https://github.com/Livox-SDK/livox_ros_driver2.git. Themen: /livox/lidar. SICK: git clone https://github.com/SICKAG/sick_scan_xd.git. Themen: /scan (2D), /cloud (3D)."}]},{"heading":{"fr":"3. Configuration réseau","en":"3. Network Configuration","de":"3. Configuration réseau","it":"3. Configurazione di rete","pt":"3. Configuração de rede","es":"3. Configuración de red","be":"3. Configuration réseau","ch":"3. Netzwerkkonfiguration","lu":"3. Netzwierk-Konfiguratioun"},"body":[{"fr":"Configurez une IP statique sur le même sous-réseau que le LiDAR (ex: 192.168.1.50/24). Activez PTP (IEEE 1588) via linuxptp pour un horodatage précis : sudo ptp4l -i eth0 -m -s. Utilisez la QoS BEST_EFFORT dans ROS2 pour les nuages de points afin d'éviter la saturation DDS.","en":"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.","de":"Richten Sie eine statische IP im selben Subnetz wie der LiDAR ein (z. B. 192.168.1.50/24). Aktivieren Sie PTP (IEEE 1588) über linuxptp für präzise Zeitstempelung: sudo ptp4l -i eth0 -m -s. Verwenden Sie BEST_EFFORT-QoS in ROS2 für Punktwolken, um eine DDS-Sättigung zu vermeiden.","it":"Configurate un IP statico sulla stessa sottorete del LiDAR (es: 192.168.1.50/24). Attivate PTP (IEEE 1588) tramite linuxptp per un timestamping preciso: sudo ptp4l -i eth0 -m -s. Utilizzate la QoS BEST_EFFORT in ROS2 per le nuvole di punti per evitare la saturazione DDS.","pt":"Configure um IP estático na mesma sub-rede que o LiDAR (ex: 192.168.1.50/24). Ative o PTP (IEEE 1588) via linuxptp para timestamping preciso: sudo ptp4l -i eth0 -m -s. Utilize a QoS BEST_EFFORT no ROS2 para as nuvens de pontos, de modo a evitar a saturação DDS.","es":"Configure una IP estática en la misma subred que el LiDAR (ej: 192.168.1.50/24). Active PTP (IEEE 1588) mediante linuxptp para una sincronización precisa: sudo ptp4l -i eth0 -m -s. Use QoS BEST_EFFORT en ROS2 para las nubes de puntos para evitar la saturación DDS.","be":"Configurez une IP statique sur le même sous-réseau que le LiDAR (ex: 192.168.1.50/24). Activez PTP (IEEE 1588) via linuxptp pour un horodatage précis : sudo ptp4l -i eth0 -m -s. Utilisez la QoS BEST_EFFORT dans ROS2 pour les nuages de points afin d\'éviter la saturation DDS.","ch":"Konfigurieren Sie eine statische IP im selben Subnetz wie der LiDAR (z.B. 192.168.1.50/24). Aktivieren Sie PTP (IEEE 1588) über linuxptp für präzise Zeitstempelung: sudo ptp4l -i eth0 -m -s. Verwenden Sie QoS BEST_EFFORT in ROS2 für Punktwolken, um DDS-Sättigung zu vermeiden.","lu":"Richten Sie eine statische IP im selben Subnetz wie der LiDAR ein (z. B. 192.168.1.50/24). Aktivieren Sie PTP (IEEE 1588) über linuxptp für präzise Zeitstempelung: sudo ptp4l -i eth0 -m -s. Verwenden Sie BEST_EFFORT-QoS in ROS2 für Punktwolken, um eine DDS-Sättigung zu vermeiden."}]},{"heading":{"fr":"4. Visualisation et traitements avancés","en":"4. Visualization and Advanced Processing","de":"4. Visualisation et traitements avancés","it":"4. Visualizzazione ed elaborazioni avanzate","pt":"4. Visualização e processamentos avançados","es":"4. Visualización y procesamiento avanzado","be":"4. Visualisation et traitements avancés","ch":"4. Visualisierung und erweiterte Verarbeitung","lu":"4. Visualiséierung a fortgeschratt Verarbeitung"},"body":[{"fr":"Visualisez le nuage de points dans RViz2 (Add > PointCloud2). Pour le SLAM 2D, utilisez slam_toolbox après projection via pointcloud_to_laserscan. Pour le SLAM 3D, utilisez Cartographer. La fusion LiDAR/IMU se fait avec robot_localization. Pour le clustering et la détection d'obstacles, utilisez PCL (pcl_ros).","en":"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).","de":"Visualisieren Sie die Punktwolke in RViz2 (Add > PointCloud2). Für 2D-SLAM verwenden Sie slam_toolbox nach Projektion über pointcloud_to_laserscan. Für 3D-SLAM verwenden Sie Cartographer. Die LiDAR/IMU-Fusion erfolgt mit robot_localization. Für Clustering und Hinderniserkennung verwenden Sie PCL (pcl_ros).","it":"Visualizzate la nuvola di punti in RViz2 (Add > PointCloud2). Per SLAM 2D, utilizzate slam_toolbox dopo proiezione tramite pointcloud_to_laserscan. Per SLAM 3D, utilizzate Cartographer. La fusione LiDAR/IMU si fa con robot_localization. Per clustering e rilevamento ostacoli, utilizzate PCL (pcl_ros).","pt":"Visualize a nuvem de pontos no RViz2 (Add > PointCloud2). Para SLAM 2D, utilize slam_toolbox após projeção via pointcloud_to_laserscan. Para SLAM 3D, utilize Cartographer. A fusão LiDAR/IMU é feita com robot_localization. Para clustering e deteção de obstáculos, utilize PCL (pcl_ros).","es":"Visualice la nube de puntos en RViz2 (Add > PointCloud2). Para SLAM 2D, use slam_toolbox después de la proyección mediante pointcloud_to_laserscan. Para SLAM 3D, use Cartographer. La fusión LiDAR/IMU se realiza con robot_localization. Para la agrupación y detección de obstáculos, use PCL (pcl_ros).","be":"Visualisez le nuage de points dans RViz2 (Add > PointCloud2). Pour le SLAM 2D, utilisez slam_toolbox après projection via pointcloud_to_laserscan. Pour le SLAM 3D, utilisez Cartographer. La fusion LiDAR/IMU se fait avec robot_localization. Pour le clustering et la détection d\'obstacles, utilisez PCL (pcl_ros).","ch":"Visualisieren Sie die Punktwolke in RViz2 (Add > PointCloud2). Für 2D-SLAM verwenden Sie slam_toolbox nach Projektion über pointcloud_to_laserscan. Für 3D-SLAM verwenden Sie Cartographer. Die LiDAR/IMU-Fusion erfolgt mit robot_localization. Für Clustering und Hinderniserkennung verwenden Sie PCL (pcl_ros).","lu":"Visualisieren Sie die Punktwolke in RViz2 (Add > PointCloud2). Für 2D-SLAM verwenden Sie slam_toolbox nach Projektion über pointcloud_to_laserscan. Für 3D-SLAM verwenden Sie Cartographer. Die LiDAR/IMU-Fusion erfolgt mit robot_localization. Für Clustering an Hinderniserkennung verwenden Sie PCL (pcl_ros)."}]},{"heading":{"fr":"5. Checklist et conclusion","en":"5. Checklist and Conclusion","de":"5. Checklist et conclusion","it":"5. Checklist e conclusione","pt":"5. Checklist e conclusão","es":"5. Lista de verificación y conclusión","be":"5. Checklist et conclusion","ch":"5. Checkliste und Fazit","lu":"5. Checklëscht a Conclusioun"},"body":[{"fr":"Vérifiez : ROS2 sourcé, driver compilé, IP statique, ping OK, PTP actif, QoS BEST_EFFORT, RViz2 fonctionnel, TF publiées et SLAM validé. La clé d'une intégration réussie : configuration réseau, QoS ROS2 et arbre TF correct.","en":"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.","de":"Überprüfen Sie: ROS2 eingerichtet, Treiber kompiliert, statische IP, Ping OK, PTP aktiv, BEST_EFFORT-QoS, RViz2 funktionsfähig, TF veröffentlicht und SLAM validiert. Schlüssel zu einer erfolgreichen Integration: Netzwerkkonfiguration, ROS2-QoS und korrekter TF-Baum.","it":"Verificate: ROS2 source attivato, driver compilato, IP statico, ping OK, PTP attivo, QoS BEST_EFFORT, RViz2 funzionante, TF pubblicati e SLAM validato. La chiave di un\'integrazione riuscita: configurazione di rete, QoS ROS2 e albero TF corretto.","pt":"Verifique: ROS2 sourceado, driver compilado, IP estático, ping OK, PTP ativo, QoS BEST_EFFORT, RViz2 funcional, TF publicadas e SLAM validado. Chave para uma integração bem-sucedida: configuração de rede, QoS ROS2 e árvore TF correta.","es":"Verifique: ROS2 en entorno, controlador compilado, IP estática, ping OK, PTP activo, QoS BEST_EFFORT, RViz2 funcional, TF publicadas y SLAM validado. La clave de una integración exitosa: configuración de red, QoS ROS2 y árbol TF correcto.","be":"Vérifiez : ROS2 sourcé, driver compilé, IP statique, ping OK, PTP actif, QoS BEST_EFFORT, RViz2 fonctionnel, TF publiées et SLAM validé. La clé d\'une intégration réussie : configuration réseau, QoS ROS2 et arbre TF correct.","ch":"Prüfen Sie: ROS2 source-d, Treiber kompiliert, statische IP, ping OK, PTP aktiv, QoS BEST_EFFORT, RViz2 funktionsfähig, TF veröffentlicht und SLAM validiert. Der Schlüssel zu einer erfolgreichen Integration: Netzwerkkonfiguration, ROS2-QoS und korrekter TF-Baum.","lu":"Überprüfen Sie: ROS2 eingerichtet, Treiber kompiliert, statische IP, Ping OK, PTP aktiv, BEST_EFFORT-QoS, RViz2 funktionsfähig, TF veröffentlicht an SLAM validiert. Schlüssel zu einer erfolgreichen Integration: Netzwerkkonfiguration, ROS2-QoS an korrekter TF-Baum."}]}]
Intégrer un LiDAR dans ROS2 : guide complet
Guide pas à pas pour intégrer un LiDAR Ouster, Hesai, Livox ou SICK dans ROS2 (Humble/Iron/Jazzy) : installation des drivers, configuration réseau, PTP, QoS, lancement, visualisation RViz2, SLAM et fusion de capteurs.
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Browse the detailed pages for each mentioned entity.
Products
Software & SDK
Flasheye
Flasheye
Edge application for LiDAR intrusion detection and counting for perimeter surveillance.
Outsight
Outsight
Real-time LiDAR perception software platform for smart city, transport and security.
RTAB-Map
OpenIntRoLab (Sherbrooke)
RGB-D and LiDAR SLAM with topological mapping and place recognition.
ROS2 Perception Pipeline
OpenOpen Robotics
ROS2 perception stack (vision, LiDAR, fusion) for mobile robots and vehicles.
NVIDIA Isaac Sim
NVIDIA
NVIDIA robotics simulator with physically accurate LiDAR sensor rendering for testing and validation.
Apollo
OpenBaidu
Open-source autonomous driving platform with LiDAR perception, HD maps and planning.
Cartographer
OpenGoogle / ROS
Google's real-time 2D and 3D SLAM library, integrated with ROS.
LIO-SAM
OpenTixiao Shan / MIT
Tightly-coupled LiDAR-IMU SLAM via factor graph optimization for high accuracy.
Autoware
OpenThe Autoware Foundation
Open-source autonomous driving framework: perception, localization, planning and control.
Hesai SDK
OpenHesai Technology
Official SDK and ROS2 drivers for Hesai LiDARs (AT, Pandar, QT, FT).
FAST-LIO / FAST-LIO2
OpenHKU MaRS Lab
Ultra-fast LiDAR-inertial SLAM with high accuracy, no additional sensor required.
RViz2
OpenOpen Robotics
The official ROS2 3D visualizer for LiDAR point clouds and other robotic data.
Exwayz Engine
Exwayz
Real-time 3D LiDAR processing SDK: SLAM, 3D mapping, sub-2 cm localization, object detection & classification. Compatible with ROS1/ROS2, Linux and Windows.
ROS2 Navigation (Nav2)
OpenOpen Robotics
ROS2 navigation framework for mobile robots: planning, control and LiDAR SLAM.
Sources
- Ouster ROS driversoftware
- Hesai ROS driversoftware
- Livox ROS Driver 2software
- SICK scan_xd driversoftware