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 Awesome LIDAR !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
 Awesome LIDAR !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
▐ A curated list of awesome LIDAR sensors and its applications.
LIDAR (https://en.wikipedia.org/wiki/Lidar) is a remote sensing sensor that uses laser light to measure the surroundings in ~cm accuracy. The sensory data is usually referred as point cloud which means set of 
data points in 3D or 2D. The list contains hardwares, datasets, point cloud-processing algorithms, point cloud frameworks, simulators etc.
LIDAR (https://en.wikipedia.org/wiki/Lidar) is a remote sensing sensor that uses laser light to measure the surroundings in ~cm accuracy. The sensory data is usually referred as point cloud which means set of data points in 3D or 2D. 
The list contains hardwares, datasets, point cloud-processing algorithms, point cloud frameworks, simulators etc.
Contributions are welcome! Please check out (contributing.md) our guidelines.
@@ -36,8 +36,8 @@
Manufacturers
- Velodyne (https://velodynelidar.com/) - Ouster and Velodyne announced the successful completion of their merger of equals, effective February 10, 2023. Velodyne was a mechanical and solid-state LIDAR 
manufacturer. The headquarter is in San Jose, California, USA.
- Velodyne (https://velodynelidar.com/) - Ouster and Velodyne announced the successful completion of their merger of equals, effective February 10, 2023. Velodyne was a mechanical and solid-state LIDAR manufacturer. The headquarter is 
in San Jose, California, USA.
 - YouTube channel :red_circle: (https://www.youtube.com/user/VelodyneLiDAR)
 - ROS driver :octocat: (https://github.com/ros-drivers/velodyne)
 - C++/Python library :octocat: (https://github.com/valgur/velodyne_decoder)
@@ -70,19 +70,18 @@
 - YouTube channel :red_circle: (https://www.youtube.com/c/IbeoAutomotive/)
- Innoviz (https://innoviz.tech/) - Innoviz technologies / specializes in solid-state LIDARs.
 - YouTube channel :red_circle: (https://www.youtube.com/channel/UCVc1KFsu2eb20M8pKFwGiFQ)
- Quanenergy (https://quanergy.com/) - Quanenergy Systems / solid-state and mechanical LIDAR sensors / offers End-to-End solutions in Mapping, Industrial Automation, Transportation and Security. The headquarter 
is located in Sunnyvale, California, USA.
- Quanenergy (https://quanergy.com/) - Quanenergy Systems / solid-state and mechanical LIDAR sensors / offers End-to-End solutions in Mapping, Industrial Automation, Transportation and Security. The headquarter is located in Sunnyvale, 
California, USA.
 - YouTube channel :red_circle: (https://www.youtube.com/c/QuanergySystems)
- Cepton (https://www.cepton.com/index.html) - Cepton (Cepton Technologies, Inc.) / pioneers in frictionless, and mirrorless design, self-developed MMT (micro motion technology) lidar technology. The headquarter
is located in San Jose, California, USA.
- Cepton (https://www.cepton.com/index.html) - Cepton (Cepton Technologies, Inc.) / pioneers in frictionless, and mirrorless design, self-developed MMT (micro motion technology) lidar technology. The headquarter is located in San Jose, 
California, USA.
 - YouTube channel :red_circle: (https://www.youtube.com/channel/UCUgkBZZ1UWWkkXJ5zD6o8QQ)
- Blickfeld (https://www.blickfeld.com/) - Blickfeld is a solid-state LIDAR manufacturer for autonomous mobility and IoT, based in München, Germany.
 - YouTube channel :red_circle: (https://www.youtube.com/c/BlickfeldLiDAR)
 - GitHub organization :octocat: (https://github.com/Blickfeld)
- Neuvition (https://www.neuvition.com/) - Neuvition is a solid-state LIDAR manufacturer based in Wujiang, China.
 - YouTube channel :red_circle: (https://www.youtube.com/channel/UClFjlekWJo4T5bfzxX0ZW3A)
- Aeva (https://www.aeva.com/) - Aeva is bringing the next wave of perception technology to all devices for automated driving, consumer electronics, health, industrial robotics and security, Mountain View, 
California, USA.
- Aeva (https://www.aeva.com/) - Aeva is bringing the next wave of perception technology to all devices for automated driving, consumer electronics, health, industrial robotics and security, Mountain View, California, USA.
 - YouTube channel :red_circle: (https://www.youtube.com/c/AevaInc)
 - GitHub organization :octocat: (https://github.com/aevainc)
- XenomatiX (https://www.xenomatix.com/) - XenomatiX offers true solid-state lidar sensors based on a multi-beam lasers concept. XenomatiX is headquartered in Leuven, Belgium.
@@ -95,8 +94,8 @@
Datasets
- Ford Dataset (https://avdata.ford.com/) - The dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. The data is Robot 
Operating System (ROS) compatible.
- Ford Dataset (https://avdata.ford.com/) - The dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. The data is Robot Operating System (ROS) 
compatible.
 - Paper :newspaper: (https://arxiv.org/pdf/2003.07969.pdf)
 - GitHub repository :octocat: (https://github.com/Ford/AVData)
- Audi A2D2 Dataset (https://www.a2d2.audi) - The dataset features 2D semantic segmentation, 3D point clouds, 3D bounding boxes, and vehicle bus data.
@@ -105,8 +104,8 @@
- Oxford RobotCar (https://robotcar-dataset.robots.ox.ac.uk/) - The Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. 
 - YouTube channel :red_circle: (https://www.youtube.com/c/ORIOxfordRoboticsInstitute)
 - Paper :newspaper: (https://robotcar-dataset.robots.ox.ac.uk/images/RCD_RTK.pdf)
- EU Long-term Dataset (https://epan-utbm.github.io/utbm_robocar_dataset/) - This dataset was collected with our robocar (in human driving mode of course), equipped up to eleven heterogeneous sensors, in the 
downtown (for long-term data) and a suburb (for roundabout data) of Montbéliard in France. The vehicle speed was limited to 50 km/h following the French traffic rules.
- EU Long-term Dataset (https://epan-utbm.github.io/utbm_robocar_dataset/) - This dataset was collected with our robocar (in human driving mode of course), equipped up to eleven heterogeneous sensors, in the downtown (for long-term 
data) and a suburb (for roundabout data) of Montbéliard in France. The vehicle speed was limited to 50 km/h following the French traffic rules.
- NuScenes (https://www.nuscenes.org/) - Public large-scale dataset for autonomous driving.
 - Paper :newspaper: (https://arxiv.org/pdf/1903.11027.pdf)
- Lyft (https://level5.lyft.com/dataset/) - Public dataset collected by a fleet of Ford Fusion vehicles equipped with LIDAR and camera.
@@ -115,27 +114,25 @@
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=3qNOXvkpK4I)
- CADC - Canadian Adverse Driving Conditions Dataset (http://cadcd.uwaterloo.ca/) - Public large-scale dataset for autonomous driving in adverse weather conditions (snowy weather).
 - Paper :newspaper: (https://arxiv.org/pdf/2001.10117.pdf)
- UofTPed50 Dataset (https://www.autodrive.utoronto.ca/uoftped50) - University of Toronto, aUToronto's self-driving car dataset, which contains GPS/IMU, 3D LIDAR, and Monocular camera data. It can be used for 3D
pedestrian detection.
- UofTPed50 Dataset (https://www.autodrive.utoronto.ca/uoftped50) - University of Toronto, aUToronto's self-driving car dataset, which contains GPS/IMU, 3D LIDAR, and Monocular camera data. It can be used for 3D pedestrian detection.
 - Paper :newspaper: (https://arxiv.org/pdf/1905.08758.pdf)
- PandaSet Open Dataset (https://scale.com/open-datasets/pandaset) - Public large-scale dataset for autonomous driving provided by Hesai & Scale. It enables researchers to study challenging urban driving 
situations using the full sensor suit of a real self-driving-car.
- Cirrus dataset (https://developer.volvocars.com/open-datasets/cirrus/) A public datatset from non-uniform distribution of LIDAR scanning patterns with emphasis on long range. In this dataset Luminar Hydra 
LIDAR is used. The dataset is available at the Volvo Cars Innovation Portal.
- PandaSet Open Dataset (https://scale.com/open-datasets/pandaset) - Public large-scale dataset for autonomous driving provided by Hesai & Scale. It enables researchers to study challenging urban driving situations using the full sensor
suit of a real self-driving-car.
- Cirrus dataset (https://developer.volvocars.com/open-datasets/cirrus/) A public datatset from non-uniform distribution of LIDAR scanning patterns with emphasis on long range. In this dataset Luminar Hydra LIDAR is used. The dataset is
available at the Volvo Cars Innovation Portal.
 - Paper :newspaper: (https://arxiv.org/pdf/2012.02938.pdf)
- USyd Dataset- The Univerisity of Sydney Campus- Dataset (http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/) - Long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis 
over the University of Sydney campus and surrounds. It includes multiple sensor modalities and covers various environmental conditions. ROS compatible
- USyd Dataset- The Univerisity of Sydney Campus- Dataset (http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/) - Long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of 
Sydney campus and surrounds. It includes multiple sensor modalities and covers various environmental conditions. ROS compatible
 - Paper :newspaper: (https://ieeexplore.ieee.org/document/9109704)
- Brno Urban Dataset :octocat: (https://github.com/Robotics-BUT/Brno-Urban-Dataset) - Navigation and localisation dataset for self driving cars and autonomous robots in Brno, Czechia.
 - Paper :newspaper: (https://ieeexplore.ieee.org/document/9197277)
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=wDFePIViwqY)
- Argoverse :octocat: (https://www.argoverse.org/) - A dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting collected in Pittsburgh, Pennsylvania and 
Miami, Florida, USA.
- Argoverse :octocat: (https://www.argoverse.org/) - A dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting collected in Pittsburgh, Pennsylvania and Miami, Florida, USA.
 - Paper :newspaper: (https://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf)
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=DM8jWfi69zM)
- Boreas Dataset (https://www.boreas.utias.utoronto.ca/) - The Boreas dataset was collected by driving a repeated route over the course of 1 year resulting in stark seasonal variations. In total, Boreas contains
over 350km of driving data including several sequences with adverse weather conditions such as rain and heavy snow. The Boreas data-taking platform features a unique high-quality sensor suite with a 128-channel 
Velodyne Alpha Prime lidar, a 360-degree Navtech radar, and accurate ground truth poses obtained from an Applanix POSLV GPS/IMU. 
- Boreas Dataset (https://www.boreas.utias.utoronto.ca/) - The Boreas dataset was collected by driving a repeated route over the course of 1 year resulting in stark seasonal variations. In total, Boreas contains over 350km of driving 
data including several sequences with adverse weather conditions such as rain and heavy snow. The Boreas data-taking platform features a unique high-quality sensor suite with a 128-channel Velodyne Alpha Prime lidar, a 360-degree 
Navtech radar, and accurate ground truth poses obtained from an Applanix POSLV GPS/IMU. 
 - Paper 📰 (https://arxiv.org/abs/2203.10168)
 - GitHub repository :octocat: (https://github.com/utiasASRL/pyboreas)
@@ -166,8 +163,8 @@
- Autoware (https://www.autoware.ai/) - Popular framework in academic and research applications of autonomous vehicles.
 - GitLab repository :octocat: (https://gitlab.com/autowarefoundation/autoware.ai)
 - Paper :newspaper: 
(https://www.researchgate.net/profile/Takuya_Azumi/publication/327198306_Autoware_on_Board_Enabling_Autonomous_Vehicles_with_Embedded_Systems/links/5c9085da45851564fae6dcd0/Autoware-on-Board-Enabling-Autonomous-
Vehicles-with-Embedded-Systems.pdf)
(https://www.researchgate.net/profile/Takuya_Azumi/publication/327198306_Autoware_on_Board_Enabling_Autonomous_Vehicles_with_Embedded_Systems/links/5c9085da45851564fae6dcd0/Autoware-on-Board-Enabling-Autonomous-Vehicles-with-Embedded-Sy
stems.pdf)
- Baidu Apollo (https://apollo.auto/) - Apollo is a popular framework which accelerates the development, testing, and deployment of Autonomous Vehicles.
 - GitHub repository :octocat: (https://github.com/ApolloAuto/apollo)
 - YouTube channel :red_circle: (https://www.youtube.com/c/ApolloAuto)
@@ -226,8 +223,8 @@
- LOAM J. Zhang and S. Singh :red_circle: (https://youtu.be/8ezyhTAEyHs) - LOAM: Lidar Odometry and Mapping in Real-time.
- LeGO-LOAM :octocat: (https://github.com/RobustFieldAutonomyLab/LeGO-LOAM) - A lightweight and ground optimized lidar odometry and mapping (LeGO-LOAM) system for ROS compatible UGVs. 
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=7uCxLUs9fwQ)
- Cartographer :octocat: (https://github.com/cartographer-project/cartographer) - Cartographer is ROS compatible system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across 
multiple platforms and sensor configurations.
- Cartographer :octocat: (https://github.com/cartographer-project/cartographer) - Cartographer is ROS compatible system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and 
sensor configurations.
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=29Knm-phAyI)
- SuMa++ :newspaper: (http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf) - LiDAR-based Semantic SLAM.
 - GitHub repository :octocat: (https://github.com/PRBonn/semantic_suma/)
@@ -246,8 +243,8 @@
- Learning to Optimally Segment Point Clouds :newspaper: (https://arxiv.org/abs/1912.04976) - By Peiyun Hu, David Held, and Deva Ramanan at Carnegie Mellon University. IEEE Robotics and Automation Letters, 2020.
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=wLxIAwIL870)
 - GitHub repository :octocat: (https://github.com/peiyunh/opcseg)
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection :newspaper: (https://arxiv.org/pdf/1809.05590.pdf) - By Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer. 
30th IEEE Intelligent Vehicles Symposium, 2019.
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection :newspaper: (https://arxiv.org/pdf/1809.05590.pdf) - By Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer. 30th IEEE Intelligent 
Vehicles Symposium, 2019.
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=2DzH9COLpkU)
- What You See is What You Get: Exploiting Visibility for 3D Object Detection :newspaper: (https://arxiv.org/pdf/1912.04986.pdf) - By Peiyun Hu, Jason Ziglar, David Held, Deva Ramanan, 2019.
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=497OF-otY2k)
@@ -265,12 +262,11 @@
- CARLA (https://carla.org/) - Unreal Engine based simulator for automotive applications. Compatible with Autoware, Baidu Apollo and ROS/ROS 2.
 - GitHub repository :octocat: (https://github.com/carla-simulator/carla)
 - YouTube channel :red_circle: (https://www.youtube.com/channel/UC1llP9ekCwt8nEJzMJBQekg)
- LGSVL / SVL (https://www.lgsvlsimulator.com/) - Unity Engine based simulator for automotive applications. Compatible with Autoware, Baidu Apollo and ROS/ROS 2. Note: LG has made the difficult decision to 
suspend (https://www.svlsimulator.com/news/2022-01-20-svl-simulator-sunset) active development of SVL Simulator.
- LGSVL / SVL (https://www.lgsvlsimulator.com/) - Unity Engine based simulator for automotive applications. Compatible with Autoware, Baidu Apollo and ROS/ROS 2. Note: LG has made the difficult decision to suspend 
(https://www.svlsimulator.com/news/2022-01-20-svl-simulator-sunset) active development of SVL Simulator.
 - GitHub repository :octocat: (https://github.com/lgsvl/simulator)
 - YouTube channel :red_circle: (https://www.youtube.com/c/LGSVLSimulator)
- OSSDC SIM (https://github.com/OSSDC/OSSDC-SIM) - Unity Engine based simulator for automotive applications, based on the suspended LGSVL simulator, but an active development. Compatible with Autoware, Baidu 
Apollo and ROS/ROS 2.
- OSSDC SIM (https://github.com/OSSDC/OSSDC-SIM) - Unity Engine based simulator for automotive applications, based on the suspended LGSVL simulator, but an active development. Compatible with Autoware, Baidu Apollo and ROS/ROS 2.
 - GitHub repository :octocat: (https://github.com/OSSDC/OSSDC-SIM)
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=fU_C38WEwGw)
- AirSim (https://microsoft.github.io/AirSim) - Unreal Engine based simulator for drones and automotive. Compatible with ROS.
@@ -304,10 +300,9 @@
 - GitHub repository :octocat: (https://github.com/CloudCompare)
- Pcx :octocat: (https://github.com/keijiro/Pcx) - Point cloud importer/renderer for Unity.
- Bpy :octocat: (https://github.com/uhlik/bpy) - Point cloud importer/renderer/editor for Blender, Point Cloud visualizer.
- Semantic Segmentation Editor :octocat: (https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor) - Point cloud and image semantic segmentation editor by Hitachi Automotive And 
Industry Laboratory, point cloud annotator / labeling.
- 3D Bounding Box Annotation Tool :octocat: (https://github.com/walzimmer/3d-bat) - 3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams, point cloud annotator / 
labeling.
- Semantic Segmentation Editor :octocat: (https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor) - Point cloud and image semantic segmentation editor by Hitachi Automotive And Industry Laboratory, point 
cloud annotator / labeling.
- 3D Bounding Box Annotation Tool :octocat: (https://github.com/walzimmer/3d-bat) - 3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams, point cloud annotator / labeling.
 - Paper :newspaper: (https://arxiv.org/pdf/1905.00525.pdf)
 - YouTube video :red_circle: (https://www.youtube.com/watch?v=gSGG4Lw8BSU)
- Photogrammetry importer :octocat: (https://github.com/SBCV/Blender-Addon-Photogrammetry-Importer) - Blender addon to import reconstruction results of several libraries.