Awesome LIDAR 
A curated list of awesome LIDAR sensors and its applications.
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.
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Contents
- Awesome LIDAR
Conventions
- Any list item with an OctoCat
has a GitHub repo or organization - Any list item with a RedCircle
🔴 has YouTube videos or channel - Any list item with a Paper
📰 has a scientific paper or detailed description
Manufacturers
- Velodyne - 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.
- Ouster - LIDAR manufacturer, specializing in digital-spinning LiDARs. Ouster is headquartered in San Francisco, USA.
- Livox - LIDAR manufacturer.
- SICK - Sensor and automation manufacturer, the headquarter is located in Waldkirch, Germany.
- Hokuyo - Sensor and automation manufacturer, headquartered in Osaka, Japan.
- Pioneer - LIDAR manufacturer, specializing in MEMS mirror-based raster scanning LiDARs (3D-LiDAR). Pioneer is headquartered in Tokyo, Japan.
- Luminar - LIDAR manufacturer focusing on compact, auto-grade sensors. Luminar is headquartered Palo Alto, California, USA.
- Hesai - Hesai Technology is a LIDAR manufacturer, founded in Shanghai, China.
- Robosense - RoboSense (Suteng Innovation Technology Co., Ltd.) is a LIDAR sensor, AI algorithm and IC chipset maufactuirer based in Shenzhen and Beijing (China).
- Ibeo - Ibeo Automotive Systems GmbH is an automotive industry / environmental detection laserscanner / LIDAR manufacturer, based in Hamburg, Germany.
- Innoviz - Innoviz technologies / specializes in solid-state LIDARs.
- Quanenergy - 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.
- Cepton - 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.
- Blickfeld - Blickfeld is a solid-state LIDAR manufacturer for autonomous mobility and IoT, based in München, Germany.
- Neuvition - Neuvition is a solid-state LIDAR manufacturer based in Wujiang, China.
- Aeva - 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.
Datasets
- Ford Dataset - 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.
- Audi A2D2 Dataset - The dataset features 2D semantic segmentation, 3D point clouds, 3D bounding boxes, and vehicle bus data.
- Waymo Open Dataset - The dataset contains independently-generated labels for lidar and camera data, not simply projections.
- Oxford RobotCar - The Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year.
- EU Long-term 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 - Public large-scale dataset for autonomous driving.
- Lyft - Public dataset collected by a fleet of Ford Fusion vehicles equipped with LIDAR and camera.
- KITTI - Widespread public dataset, pirmarily focusing on computer vision applications, but also contains LIDAR point cloud.
- Semantic KITTI - Dataset for semantic and panoptic scene segmentation.
- CADC - Canadian Adverse Driving Conditions Dataset - Public large-scale dataset for autonomous driving in adverse weather conditions (snowy weather).
- UofTPed50 Dataset - 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.
- PandaSet Open Dataset - 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 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.
- USyd Dataset- The Univerisity of Sydney 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
- Brno Urban Dataset - Navigation and localisation dataset for self driving cars and autonomous robots in Brno, Czechia.
- Argoverse - A dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting collected in Pittsburgh, Pennsylvania and Miami, Florida, USA.
- Boreas Dataset - 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.
Libraries
- Point Cloud Library (PCL) - Popular highly parallel programming library, with numerous industrial and research use-cases.
- Open3D library - Open3D library contanins 3D data processing and visualization algorithms. It is open-source and supports both C++ and Python.
- PyTorch Geometric
📰 - A geometric deep learning extension library for PyTorch. - PyTorch3d - PyTorch3d is a library for deep learning with 3D data written and maintained by the Facebook AI Research Computer Vision Team.
- Kaolin - Kaolin is a PyTorch Library for Accelerating 3D Deep Learning Research written by NVIDIA Technologies for game and application developers.
- PyVista - 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit.
- pyntcloud - Pyntcloud is a Python 3 library for working with 3D point clouds leveraging the power of the Python scientific stack.
- pointcloudset - Python library for efficient analysis of large datasets of point clouds recorded over time.
Frameworks
- Autoware - Popular framework in academic and research applications of autonomous vehicles.
- Baidu Apollo - Apollo is a popular framework which accelerates the development, testing, and deployment of Autonomous Vehicles.
Algorithms
Basic matching algorithms
- Iterative closest point (ICP)
🔴 - The must-have algorithm for feature matching applications (ICP).- GitHub repository - simpleICP C++ /Julia / Matlab / Octave / Python implementation.
- GitHub repository - libpointmatcher, a modular library implementing the ICP algorithm.
- Paper
📰 - libpointmatcher: Comparing ICP variants on real-world data sets.
- GitHub repository
- Normal distributions transform
🔴 - More recent massively-parallel approach to feature matching (NDT). - KISS-ICP
🔴 - In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way.
Semantic segmentation
- RangeNet++
📰 - Fast and Accurate LiDAR Sematnic Segmentation with fully convolutional network. - PolarNet
📰 - An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. - Frustum PointNets
📰 - Frustum PointNets for 3D Object Detection from RGB-D Data. - Study of LIDAR Semantic Segmentation - Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study IV 2020.
- LIDAR-MOS
📰 - Moving Object Segmentation in 3D LIDAR Data - SuperPoint Graph
📰 - Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs - RandLA-Net
📰 - Efficient Semantic Segmentation of Large-Scale Point Clouds - Automatic labelling
📰 - Automatic labelling of urban point clouds using data fusion
Ground segmentation
- Plane Seg - ROS comapatible ground plane segmentation; a library for fitting planes to LIDAR.
- LineFit Graph
📰 - Line fitting-based fast ground segmentation for horizontal 3D LiDAR data - Patchwork
📰 - Region-wise plane fitting-based robust and fast ground segmentation for 3D LiDAR data - Patchwork++
📰 - Improved version of Patchwork. Patchwork++ provides pybinding as well for deep learning users
Simultaneous localization and mapping SLAM and LIDAR-based odometry and or mapping LOAM
- LOAM J. Zhang and S. Singh
🔴 - LOAM: Lidar Odometry and Mapping in Real-time. - LeGO-LOAM - A lightweight and ground optimized lidar odometry and mapping (LeGO-LOAM) system for ROS compatible UGVs.
- 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.
- SuMa++
📰 - LiDAR-based Semantic SLAM. - OverlapNet
📰 - Loop Closing for LiDAR-based SLAM. - LIO-SAM
📰 - Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping. - Removert
📰 - Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images.
Object detection and object tracking
- Learning to Optimally Segment Point Clouds
📰 - By Peiyun Hu, David Held, and Deva Ramanan at Carnegie Mellon University. IEEE Robotics and Automation Letters, 2020. - Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
📰 - By Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer. 30th IEEE Intelligent Vehicles Symposium, 2019. - What You See is What You Get: Exploiting Visibility for 3D Object Detection
📰 - By Peiyun Hu, Jason Ziglar, David Held, Deva Ramanan, 2019. - urban_road_filter
📰 - Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles
Simulators
- CoppeliaSim - Cross-platform general-purpose robotic simulator (formerly known as V-REP).
- OSRF Gazebo - OGRE-based general-purpose robotic simulator, ROS/ROS 2 compatible.
- CARLA - Unreal Engine based simulator for automotive applications. Compatible with Autoware, Baidu Apollo and ROS/ROS 2.
- LGSVL / SVL - 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 active development of SVL Simulator.
- 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.
- AirSim - Unreal Engine based simulator for drones and automotive. Compatible with ROS.
- AWSIM - Unity Engine based simulator for automotive applications. Compatible with Autoware and ROS 2.
Related awesome
- Awesome point cloud analysis
- Awesome robotics
- Awesome robotics libraries
- Awesome ROS 2
- Awesome artificial intelligence
- Awesome computer vision
- Awesome machine learning
- Awesome deep learning
- Awesome reinforcement learning
- Awesome SLAM datasets
- Awesome electronics
- Awesome vehicle security and car hacking
- Awesome LIDAR-Camera calibration
Others
- ARHeadsetKit - Using $5 Google Cardboard to replicate Microsoft Hololens. Hosts the source code for research on scene color reconstruction.
- Pointcloudprinter - A tool to turn point cloud data from aerial lidar scans into solid meshes for 3D printing.
- CloudCompare - CloudCompare is a free, cross-platform point cloud editor software.
- Pcx - Point cloud importer/renderer for Unity.
- Bpy - Point cloud importer/renderer/editor for Blender, Point Cloud visualizer.
- 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 - 3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams, point cloud annotator / labeling.
- Photogrammetry importer - Blender addon to import reconstruction results of several libraries.
- Foxglove - Foxglove Studio is an integrated visualization and diagnosis tool for robotics, available in your browser or for download as a desktop app on Linux, Windows, and macOS.
- MeshLab - MeshLab is an open source, portable, and extensible system for the processing and editing 3D triangular meshes and pointcloud.