1192 lines
56 KiB
HTML
1192 lines
56 KiB
HTML
<h1 id="awesome-lidar-awesome">Awesome LIDAR <a
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href="https://awesome.re"><img src="https://awesome.re/badge-flat.svg"
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alt="Awesome" /></a></h1>
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<p><img src="img/lidar.svg" align="right" width="100"></p>
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<blockquote>
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<p>A curated list of awesome LIDAR sensors and its applications.</p>
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</blockquote>
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<p><a href="https://en.wikipedia.org/wiki/Lidar">LIDAR</a> is a remote
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sensing sensor that uses laser light to measure the surroundings in ~cm
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accuracy. The sensory data is usually referred as point cloud which
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means set of data points in 3D or 2D. The list contains hardwares,
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datasets, point cloud-processing algorithms, point cloud frameworks,
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simulators etc.</p>
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<p>Contributions are welcome! Please <a href="contributing.md">check
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out</a> our guidelines.</p>
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<blockquote>
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<p>[!TIP] An optional view: <a
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href="https://www.trackawesomelist.com/szenergy/awesome-lidar/readme/">trackawesomelist.com/szenergy/awesome-lidar</a></p>
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</blockquote>
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<h2 id="contents">Contents</h2>
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<ul>
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<li><a href="#awesome-lidar-">Awesome LIDAR</a>
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<ul>
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<li><a href="#contents">Contents</a></li>
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<li><a href="#conventions">Conventions</a></li>
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<li><a href="#manufacturers">Manufacturers</a></li>
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<li><a href="#datasets">Datasets</a></li>
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<li><a href="#libraries">Libraries</a></li>
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<li><a href="#frameworks">Frameworks</a></li>
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<li><a href="#algorithms">Algorithms</a>
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<ul>
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<li><a href="#basic-matching-algorithms">Basic matching
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algorithms</a></li>
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<li><a href="#semantic-segmentation">Semantic segmentation</a></li>
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<li><a href="#ground-segmentation">Ground segmentation</a></li>
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<li><a
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href="#simultaneous-localization-and-mapping-slam-and-lidar-based-odometry-and-or-mapping-loam">Simultaneous
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localization and mapping SLAM and LIDAR-based odometry and or mapping
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LOAM</a></li>
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<li><a href="#object-detection-and-object-tracking">Object detection and
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object tracking</a></li>
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<li><a href="#lidar-other-sensor-calibration">LIDAR-other-sensor
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calibration</a></li>
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</ul></li>
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<li><a href="#simulators">Simulators</a></li>
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<li><a href="#related-awesome">Related awesome</a></li>
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<li><a href="#others">Others</a></li>
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</ul></li>
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</ul>
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<h2 id="conventions">Conventions</h2>
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<ul>
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<li>Any list item with <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" />
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badge has a GitHub repo or organization</li>
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<li>Any list item with <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" />
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badge has YouTube videos or channel</li>
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<li>Any list item with <img
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src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" />
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badge has a scientific paper or detailed description</li>
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<li>Any list item with <img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" />
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badge is <a href="https://docs.ros.org/"><code>ROS 2</code></a>
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compatible</li>
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</ul>
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<h2 id="manufacturers">Manufacturers</h2>
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<ul>
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<li><a href="https://velodynelidar.com/">Velodyne</a> - Ouster and
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Velodyne announced the successful completion of their <em>merger</em> of
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equals, effective February 10, 2023. Velodyne was a mechanical and
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solid-state LIDAR manufacturer. The headquarter is in San Jose,
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California, USA.
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<ul>
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<li><a href="https://www.youtube.com/user/VelodyneLiDAR">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/ros-drivers/velodyne">ROS driver <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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<li><a href="https://github.com/valgur/velodyne_decoder">C++/Python
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library <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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</ul></li>
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<li><a href="https://ouster.com/">Ouster</a> - LIDAR manufacturer,
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specializing in digital-spinning LiDARs. Ouster is headquartered in San
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Francisco, USA.
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<ul>
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<li><a href="https://www.youtube.com/c/Ouster-lidar">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/ouster-lidar">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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<li><a href="https://www.livoxtech.com/">Livox</a> - LIDAR manufacturer.
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UCnLpB5QxlQUexi40vM12mNQ">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/Livox-SDK">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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<li><a href="https://www.sick.com/ag/en/">SICK</a> - Sensor and
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automation manufacturer, the headquarter is located in Waldkirch,
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Germany.
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<ul>
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<li><a href="https://www.youtube.com/user/SICKSensors">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/SICKAG">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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<li><a href="https://www.hokuyo-aut.jp/">Hokuyo</a> - Sensor and
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automation manufacturer, headquartered in Osaka, Japan.
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UCYzJXC82IEy-h-io2REin5g">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="http://autonomousdriving.pioneer/en/3d-lidar/">Pioneer</a>
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- LIDAR manufacturer, specializing in MEMS mirror-based raster scanning
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LiDARs (3D-LiDAR). Pioneer is headquartered in Tokyo, Japan.
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<ul>
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<li><a href="https://www.youtube.com/user/PioneerCorporationPR">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://www.luminartech.com/">Luminar</a> - LIDAR
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manufacturer focusing on compact, auto-grade sensors. Luminar is
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headquartered Palo Alto, California, USA.
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<ul>
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<li><a href="https://vimeo.com/luminartech">Vimeo channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/luminartech">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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</ul></li>
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<li><a href="https://www.hesaitech.com/">Hesai</a> - Hesai Technology is
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a LIDAR manufacturer, founded in Shanghai, China.
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UCG2_ffm6sdMsK-FX8yOLNYQ/videos">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/HesaiTechnology">GitHub organization
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<img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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</ul></li>
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<li><a href="http://www.robosense.ai/">Robosense</a> - RoboSense (Suteng
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Innovation Technology Co., Ltd.) is a LIDAR sensor, AI algorithm and IC
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chipset maufactuirer based in Shenzhen and Beijing (China).
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UCYCK8j678N6d_ayWE_8F3rQ">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/RoboSense-LiDAR">GitHub organization
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<img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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<li><a href="https://www.lslidar.com/">LSLIDAR</a> - LSLiDAR (Leishen
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Intelligent System Co., Ltd.) is a LIDAR sensor manufacturer and
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complete solution provider based in Shenzhen, China.
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<ul>
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<li><a href="https://www.youtube.com/@lslidar2015">YouTube channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/Lslidar">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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<li><a href="https://www.ibeo-as.com/">Ibeo</a> - Ibeo Automotive
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Systems GmbH is an automotive industry / environmental detection
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laserscanner / LIDAR manufacturer, based in Hamburg, Germany.
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<ul>
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<li><a href="https://www.youtube.com/c/IbeoAutomotive/">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://innoviz.tech/">Innoviz</a> - Innoviz technologies /
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specializes in solid-state LIDARs.
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UCVc1KFsu2eb20M8pKFwGiFQ">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://quanergy.com/">Quanenergy</a> - Quanenergy Systems
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/ solid-state and mechanical LIDAR sensors / offers End-to-End solutions
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in Mapping, Industrial Automation, Transportation and Security. The
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headquarter is located in Sunnyvale, California, USA.
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<ul>
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<li><a href="https://www.youtube.com/c/QuanergySystems">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://www.cepton.com/index.html">Cepton</a> - Cepton
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(Cepton Technologies, Inc.) / pioneers in frictionless, and mirrorless
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design, self-developed MMT (micro motion technology) lidar technology.
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The headquarter is located in San Jose, California, USA.
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UCUgkBZZ1UWWkkXJ5zD6o8QQ">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://www.blickfeld.com/">Blickfeld</a> - Blickfeld is a
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solid-state LIDAR manufacturer for autonomous mobility and IoT, based in
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München, Germany.
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<ul>
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<li><a href="https://www.youtube.com/c/BlickfeldLiDAR">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/Blickfeld">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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<li><a href="https://www.neuvition.com/">Neuvition</a> - Neuvition is a
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solid-state LIDAR manufacturer based in Wujiang, China.
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<ul>
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<li><a
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href="https://www.youtube.com/channel/UClFjlekWJo4T5bfzxX0ZW3A">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://www.aeva.com/">Aeva</a> - Aeva is bringing the next
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wave of perception technology to all devices for automated driving,
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consumer electronics, health, industrial robotics and security, Mountain
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View, California, USA.
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<ul>
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<li><a href="https://www.youtube.com/c/AevaInc">YouTube channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/aevainc">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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</ul></li>
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<li><a href="https://www.xenomatix.com/">XenomatiX</a> - XenomatiX
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offers true solid-state lidar sensors based on a multi-beam lasers
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concept. XenomatiX is headquartered in Leuven, Belgium.
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<ul>
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<li><a
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href="https://www.youtube.com/@XenomatiXTruesolidstatelidar">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://microvision.com/">MicroVision</a> - A pioneer in
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MEMS-based laser beam scanning technology, the main focus is on building
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Automotive grade Lidar sensors, located in Hamburg, Germany.
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<ul>
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<li><a href="https://www.youtube.com/user/mvisvideo">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/MicroVision-Inc">GitHub organization
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<img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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</ul></li>
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<li><a href="https://www.preact-tech.com/">PreAct</a> - PreAct’s mission
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is to make life safer and more efficient for the automotive industry and
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beyond. The headquarter is located in Portland, Oregon, USA.
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<ul>
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<li><a href="https://www.youtube.com/@PreActTechnologies">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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</ul></li>
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<li><a href="https://www.pepperl-fuchs.com/">Pepperl+Fuchs</a> - Is a
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global technology company, specialized in innovative automation
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solutions and sensor technologies, such as LiDAR, based in Mannheim,
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Germany.
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<ul>
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<li><a href="https://www.youtube.com/c/pepperl-fuchs">YouTube channel
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<img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://www.youtube.com/user/PepperlFuchsUSA">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a href="https://github.com/PepperlFuchs">GitHub organization <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
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<img
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src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
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</ul></li>
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</ul>
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<h2 id="datasets">Datasets</h2>
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<ul>
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<li><a href="https://avdata.ford.com/">Ford Dataset</a> - The dataset is
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time-stamped and contains raw data from all the sensors, calibration
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values, pose trajectory, ground truth pose, and 3D maps. The data is
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Robot Operating System (ROS) compatible.
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<ul>
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<li><a href="https://arxiv.org/pdf/2003.07969.pdf">Paper <img
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src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
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<li><a href="https://github.com/Ford/AVData">GitHub repository <img
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src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
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</ul></li>
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<li><a href="https://www.a2d2.audi">Audi A2D2 Dataset</a> - The dataset
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features 2D semantic segmentation, 3D point clouds, 3D bounding boxes,
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and vehicle bus data.
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<ul>
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<li><a
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href="https://www.a2d2.audi/content/dam/a2d2/dataset/a2d2-audi-autonomous-driving-dataset.pdf">Paper
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<img
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src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
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</ul></li>
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<li><a href="https://waymo.com/open/">Waymo Open Dataset</a> - The
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dataset contains independently-generated labels for lidar and camera
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data, not simply projections.</li>
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<li><a href="https://robotcar-dataset.robots.ox.ac.uk/">Oxford
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RobotCar</a> - The Oxford RobotCar Dataset contains over 100 repetitions
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of a consistent route through Oxford, UK, captured over a period of over
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a year.
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<ul>
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<li><a
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href="https://www.youtube.com/c/ORIOxfordRoboticsInstitute">YouTube
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channel <img
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src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
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<li><a
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href="https://robotcar-dataset.robots.ox.ac.uk/images/RCD_RTK.pdf">Paper
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<img
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src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
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</ul></li>
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<li><a href="https://epan-utbm.github.io/utbm_robocar_dataset/">EU
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Long-term Dataset</a> - This dataset was collected with our robocar (in
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human driving mode of course), equipped up to eleven heterogeneous
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sensors, in the downtown (for long-term data) and a suburb (for
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roundabout data) of Montbéliard in France. The vehicle speed was limited
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to 50 km/h following the French traffic rules.</li>
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<li><a href="https://www.nuscenes.org/">NuScenes</a> - Public
|
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large-scale dataset for autonomous driving.
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<ul>
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<li><a href="https://arxiv.org/pdf/1903.11027.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://level5.lyft.com/dataset/">Lyft</a> - Public dataset
|
||
collected by a fleet of Ford Fusion vehicles equipped with LIDAR and
|
||
camera.</li>
|
||
<li><a
|
||
href="http://www.cvlibs.net/datasets/kitti/raw_data.php">KITTI</a> -
|
||
Widespread public dataset, pirmarily focusing on computer vision
|
||
applications, but also contains LIDAR point cloud. <img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="http://semantic-kitti.org/">Semantic KITTI</a> - Dataset
|
||
for semantic and panoptic scene segmentation.
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=3qNOXvkpK4I">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="http://cadcd.uwaterloo.ca/">CADC - Canadian Adverse Driving
|
||
Conditions Dataset</a> - Public large-scale dataset for autonomous
|
||
driving in adverse weather conditions (snowy weather).
|
||
<ul>
|
||
<li><a href="https://arxiv.org/pdf/2001.10117.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://www.autodrive.utoronto.ca/uoftped50">UofTPed50
|
||
Dataset</a> - 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.
|
||
<ul>
|
||
<li><a href="https://arxiv.org/pdf/1905.08758.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://scale.com/open-datasets/pandaset">PandaSet Open
|
||
Dataset</a> - 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.</li>
|
||
<li><a
|
||
href="https://developer.volvocars.com/open-datasets/cirrus/">Cirrus
|
||
dataset</a> 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.
|
||
<ul>
|
||
<li><a href="https://arxiv.org/pdf/2012.02938.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/">USyd
|
||
Dataset- The Univerisity of Sydney Campus- Dataset</a> - 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
|
||
<ul>
|
||
<li><a href="https://ieeexplore.ieee.org/document/9109704">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://github.com/Robotics-BUT/Brno-Urban-Dataset">Brno
|
||
Urban Dataset <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- Navigation and localisation dataset for self driving cars and
|
||
autonomous robots in Brno, Czechia.
|
||
<ul>
|
||
<li><a href="https://ieeexplore.ieee.org/document/9197277">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=wDFePIViwqY">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://www.argoverse.org/">Argoverse <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- A dataset designed to support autonomous vehicle perception tasks
|
||
including 3D tracking and motion forecasting collected in Pittsburgh,
|
||
Pennsylvania and Miami, Florida, USA.
|
||
<ul>
|
||
<li><a
|
||
href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf">Paper
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=DM8jWfi69zM">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://www.boreas.utias.utoronto.ca/">Boreas Dataset</a> -
|
||
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.
|
||
<ul>
|
||
<li><a href="https://arxiv.org/abs/2203.10168">Paper 📰</a></li>
|
||
<li><a href="https://github.com/utiasASRL/pyboreas">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="libraries">Libraries</h2>
|
||
<ul>
|
||
<li><a href="http://www.pointclouds.org/">Point Cloud Library (PCL)</a>
|
||
- Popular highly parallel programming library, with numerous industrial
|
||
and research use-cases.
|
||
<ul>
|
||
<li><a href="https://github.com/PointCloudLibrary/pcl">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
</ul></li>
|
||
<li><a href="http://www.open3d.org/docs/release/">Open3D library</a> -
|
||
Open3D library contanins 3D data processing and visualization
|
||
algorithms. It is open-source and supports both C++ and Python.
|
||
<ul>
|
||
<li><a href="https://github.com/intel-isl/Open3D">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://www.youtube.com/channel/UCRJBlASPfPBtPXJSPffJV-w">YouTube
|
||
channel <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/1903.02428.pdf">PyTorch Geometric
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- A geometric deep learning extension library for PyTorch.
|
||
<ul>
|
||
<li><a href="https://github.com/rusty1s/pytorch_geometric">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://pytorch3d.org/">PyTorch3d</a> - PyTorch3d is a
|
||
library for deep learning with 3D data written and maintained by the
|
||
Facebook AI Research Computer Vision Team.
|
||
<ul>
|
||
<li><a href="https://github.com/facebookresearch/pytorch3d">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://kaolin.readthedocs.io/en/latest/">Kaolin</a> -
|
||
Kaolin is a PyTorch Library for Accelerating 3D Deep Learning Research
|
||
written by NVIDIA Technologies for game and application developers.
|
||
<ul>
|
||
<li><a href="https://github.com/NVIDIAGameWorks/kaolin/">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://arxiv.org/pdf/1911.05063.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://docs.pyvista.org/">PyVista</a> - 3D plotting and
|
||
mesh analysis through a streamlined interface for the Visualization
|
||
Toolkit.
|
||
<ul>
|
||
<li><a href="https://github.com/pyvista/pyvista">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://joss.theoj.org/papers/10.21105/joss.01450">Paper
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://pyntcloud.readthedocs.io/en/latest/">pyntcloud</a>
|
||
- Pyntcloud is a Python 3 library for working with 3D point clouds
|
||
leveraging the power of the Python scientific stack.
|
||
<ul>
|
||
<li><a href="https://github.com/daavoo/pyntcloud">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="https://virtual-vehicle.github.io/pointcloudset/">pointcloudset</a>
|
||
- Python library for efficient analysis of large datasets of point
|
||
clouds recorded over time.
|
||
<ul>
|
||
<li><a href="https://github.com/virtual-vehicle/pointcloudset">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://rapidlasso.de/lastools/">LAStools</a> - C++ library
|
||
and command-line tools for pointcloud processing and data compressing.
|
||
<ul>
|
||
<li><a href="https://github.com/LAStools/LAStools">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="frameworks">Frameworks</h2>
|
||
<ul>
|
||
<li><a href="https://www.autoware.ai/">Autoware</a> - Popular framework
|
||
in academic and research applications of autonomous vehicles.
|
||
<ul>
|
||
<li><a href="https://github.com/autowarefoundation">GitHub oragnization
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a
|
||
href="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">Paper
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://apollo.auto/">Baidu Apollo</a> - Apollo is a
|
||
popular framework which accelerates the development, testing, and
|
||
deployment of Autonomous Vehicles.
|
||
<ul>
|
||
<li><a href="https://github.com/ApolloAuto/apollo">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/c/ApolloAuto">YouTube channel <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://ieeexplore.ieee.org/document/11024231">ALFA
|
||
Framework <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- An open-source framework for developing processing algorithms, with a
|
||
focus on embedded platforms and hardware acceleration.
|
||
<ul>
|
||
<li><a href="https://github.com/alfa-project/alfa-framework">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" />
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="algorithms">Algorithms</h2>
|
||
<h3 id="basic-matching-algorithms">Basic matching algorithms</h3>
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=uzOCS_gdZuM">Iterative
|
||
closest point (ICP) <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a>
|
||
- The must-have algorithm for feature matching applications (ICP).
|
||
<ul>
|
||
<li><a href="https://github.com/pglira/simpleICP">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- simpleICP C++ /Julia / Matlab / Octave / Python implementation.</li>
|
||
<li><a href="https://github.com/ethz-asl/libpointmatcher">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- libpointmatcher, a modular library implementing the ICP
|
||
algorithm.</li>
|
||
<li><a
|
||
href="https://link.springer.com/content/pdf/10.1007/s10514-013-9327-2.pdf">Paper
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- libpointmatcher: Comparing ICP variants on real-world data sets.</li>
|
||
</ul></li>
|
||
<li><a href="https://www.youtube.com/watch?v=0YV4a2asb8Y">Normal
|
||
distributions transform <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a>
|
||
- More recent massively-parallel approach to feature matching
|
||
(NDT).</li>
|
||
<li><a href="https://www.youtube.com/watch?v=kMMH8rA1ggI">KISS-ICP <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a>
|
||
- In Defense of Point-to-Point ICP – Simple, Accurate, and Robust
|
||
Registration If Done the Right Way.
|
||
<ul>
|
||
<li><a href="https://github.com/PRBonn/kiss-icp">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://arxiv.org/pdf/2209.15397.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="semantic-segmentation">Semantic segmentation</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf">RangeNet++
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Fast and Accurate LiDAR Sematnic Segmentation with fully convolutional
|
||
network.
|
||
<ul>
|
||
<li><a href="https://github.com/PRBonn/rangenet_lib">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=uo3ZuLuFAzk">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2003.14032.pdf">PolarNet <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- An Improved Grid Representation for Online LiDAR Point Clouds Semantic
|
||
Segmentation.
|
||
<ul>
|
||
<li><a href="https://github.com/edwardzhou130/PolarSeg">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=iIhttRSMqjE">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/1711.08488.pdf">Frustum PointNets
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Frustum PointNets for 3D Object Detection from RGB-D Data.
|
||
<ul>
|
||
<li><a href="https://github.com/charlesq34/frustum-pointnets">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://larissa.triess.eu/scan-semseg/">Study of LIDAR
|
||
Semantic Segmentation</a> - Scan-based Semantic Segmentation of LiDAR
|
||
Point Clouds: An Experimental Study IV 2020.
|
||
<ul>
|
||
<li><a href="https://arxiv.org/abs/2004.11803">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
<li><a href="http://ltriess.github.io/scan-semseg">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="https://www.ipb.uni-bonn.de/pdfs/chen2021ral-iros.pdf">LIDAR-MOS
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Moving Object Segmentation in 3D LIDAR Data
|
||
<ul>
|
||
<li><a href="https://github.com/PRBonn/LiDAR-MOS">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=NHvsYhk4dhw">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/1711.09869.pdf">SuperPoint Graph <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>-
|
||
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
|
||
<ul>
|
||
<li><a href="https://github.com/loicland/superpoint_graph">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=Ijr3kGSU_tU">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2306.08045.pdf">SuperPoint
|
||
Transformer <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>-
|
||
Efficient 3D Semantic Segmentation with Superpoint Transformer
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/drprojects/superpoint_transformer">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=2qKhpQs9gJw">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/1911.11236.pdf">RandLA-Net <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Efficient Semantic Segmentation of Large-Scale Point Clouds
|
||
<ul>
|
||
<li><a href="https://github.com/QingyongHu/RandLA-Net">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=Ar3eY_lwzMk">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2108.13757.pdf">Automatic labelling
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Automatic labelling of urban point clouds using data fusion
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/Amsterdam-AI-Team/Urban_PointCloud_Processing">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=qMj_WM6D0vI">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="ground-segmentation">Ground segmentation</h3>
|
||
<ul>
|
||
<li><a href="https://github.com/ori-drs/plane_seg">Plane Seg <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- ROS comapatible ground plane segmentation; a library for fitting
|
||
planes to LIDAR.
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=YYs4lJ9t-Xo">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/abstract/document/5548059">LineFit
|
||
Graph <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>-
|
||
Line fitting-based fast ground segmentation for horizontal 3D LiDAR data
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/lorenwel/linefit_ground_segmentation">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2108.05560.pdf">Patchwork <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>-
|
||
Region-wise plane fitting-based robust and fast ground segmentation for
|
||
3D LiDAR data
|
||
<ul>
|
||
<li><a href="https://github.com/LimHyungTae/patchwork">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=rclqeDi4gow">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2207.11919.pdf">Patchwork++ <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>-
|
||
Improved version of Patchwork. Patchwork++ provides pybinding as well
|
||
for deep learning users
|
||
<ul>
|
||
<li><a href="https://github.com/url-kaist/patchwork-plusplus-ros">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=fogCM159GRk">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3
|
||
id="simultaneous-localization-and-mapping-slam-and-lidar-based-odometry-and-or-mapping-loam">Simultaneous
|
||
localization and mapping SLAM and LIDAR-based odometry and or mapping
|
||
LOAM</h3>
|
||
<ul>
|
||
<li><a href="https://youtu.be/8ezyhTAEyHs">LOAM J. Zhang and S. Singh
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a>
|
||
- LOAM: Lidar Odometry and Mapping in Real-time.</li>
|
||
<li><a
|
||
href="https://github.com/RobustFieldAutonomyLab/LeGO-LOAM">LeGO-LOAM
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- A lightweight and ground optimized lidar odometry and mapping
|
||
(LeGO-LOAM) system for ROS compatible UGVs.
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=7uCxLUs9fwQ">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
<li>ROS 2 verison on different repo: <a
|
||
href="https://github.com/eperdices/LeGO-LOAM-SR">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="https://github.com/cartographer-project/cartographer">Cartographer
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- Cartographer is ROS compatible system that provides real-time
|
||
simultaneous localization and mapping (SLAM) in 2D and 3D across
|
||
multiple platforms and sensor configurations. <img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" />
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=29Knm-phAyI">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf">SuMa++
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- LiDAR-based Semantic SLAM.
|
||
<ul>
|
||
<li><a href="https://github.com/PRBonn/semantic_suma/">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://youtu.be/uo3ZuLuFAzk">YouTube video <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020rss.pdf">OverlapNet
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Loop Closing for LiDAR-based SLAM.
|
||
<ul>
|
||
<li><a href="https://github.com/PRBonn/OverlapNet">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=YTfliBco6aw">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2007.00258.pdf">LIO-SAM <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping.
|
||
<ul>
|
||
<li><a href="https://github.com/TixiaoShan/LIO-SAM">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=A0H8CoORZJU">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="http://ras.papercept.net/images/temp/IROS/files/0855.pdf">Removert
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Remove, then Revert: Static Point cloud Map Construction using
|
||
Multiresolution Range Images.
|
||
<ul>
|
||
<li><a href="https://github.com/irapkaist/removert">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=M9PEGi5fAq8">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/2504.11580">RESPLE <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- Recursive Spline Estimation for LiDAR-Based Odometry
|
||
<ul>
|
||
<li><a href="https://github.com/ASIG-X/RESPLE">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=3-xLRRT25ys">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="object-detection-and-object-tracking">Object detection and
|
||
object tracking</h3>
|
||
<ul>
|
||
<li><a href="https://arxiv.org/abs/1912.04976">Learning to Optimally
|
||
Segment Point Clouds <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- By Peiyun Hu, David Held, and Deva Ramanan at Carnegie Mellon
|
||
University. IEEE Robotics and Automation Letters, 2020.
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=wLxIAwIL870">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
<li><a href="https://github.com/peiyunh/opcseg">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/1809.05590.pdf">Leveraging
|
||
Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D
|
||
Object Detection <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- By Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer. 30th IEEE
|
||
Intelligent Vehicles Symposium, 2019.
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=2DzH9COLpkU">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://arxiv.org/pdf/1912.04986.pdf">What You See is What
|
||
You Get: Exploiting Visibility for 3D Object Detection <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- By Peiyun Hu, Jason Ziglar, David Held, Deva Ramanan, 2019.
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/watch?v=497OF-otY2k">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
<li><a href="https://github.com/peiyunh/WYSIWYG">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://doi.org/10.3390/s22010194">urban_road_filter <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>-
|
||
Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous
|
||
Vehicles
|
||
<ul>
|
||
<li><a href="https://github.com/jkk-research/urban_road_filter">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=T2qi4pldR-E">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851">detection_by_tracker
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a>
|
||
- 3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target
|
||
Detection and Tracking under Urban Road Uncertainties, also used in
|
||
Autoware Universe
|
||
<ul>
|
||
<li><a
|
||
href="https://autowarefoundation.github.io/autoware.universe/main/perception/detection_by_tracker/">GitHub
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=xSGCpb24dhI">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="lidar-other-sensor-calibration">LIDAR-other-sensor
|
||
calibration</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://koide3.github.io/direct_visual_lidar_calibration/">direct_visual_lidar_calibration</a>
|
||
- General, Single-shot, Target-less, and Automatic LiDAR-Camera
|
||
Extrinsic Calibration Toolbox
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/koide3/direct_visual_lidar_calibration">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a
|
||
href="https://staff.aist.go.jp/k.koide/assets/pdf/icra2023.pdf">Paper
|
||
<img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://github.com/PJLab-ADG/SensorsCalibration">OpenCalib
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- A Multi-sensor Calibration Toolbox for Autonomous Driving
|
||
<ul>
|
||
<li><a href="https://arxiv.org/pdf/2205.14087">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="simulators">Simulators</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://www.coppeliarobotics.com/coppeliaSim">CoppeliaSim</a> -
|
||
Cross-platform general-purpose robotic simulator (formerly known as
|
||
V-REP).
|
||
<ul>
|
||
<li><a href="https://www.youtube.com/user/VirtualRobotPlatform">YouTube
|
||
channel <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="http://gazebosim.org/">OSRF Gazebo</a> - OGRE-based
|
||
general-purpose robotic simulator, ROS/ROS 2 compatible.
|
||
<ul>
|
||
<li><a href="https://github.com/osrf/gazebo">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
</ul></li>
|
||
<li><a href="https://carla.org/">CARLA</a> - Unreal Engine based
|
||
simulator for automotive applications. Compatible with Autoware, Baidu
|
||
Apollo and ROS/ROS 2.
|
||
<ul>
|
||
<li><a href="https://github.com/carla-simulator/carla">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a
|
||
href="https://www.youtube.com/channel/UC1llP9ekCwt8nEJzMJBQekg">YouTube
|
||
channel <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://www.lgsvlsimulator.com/">LGSVL / SVL</a> - Unity
|
||
Engine based simulator for automotive applications. Compatible with
|
||
Autoware, Baidu Apollo and ROS/ROS 2. <em>Note:</em> LG has made the
|
||
difficult decision to <a
|
||
href="https://www.svlsimulator.com/news/2022-01-20-svl-simulator-sunset">suspend</a>
|
||
active development of SVL Simulator.
|
||
<ul>
|
||
<li><a href="https://github.com/lgsvl/simulator">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/c/LGSVLSimulator">YouTube channel
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://github.com/OSSDC/OSSDC-SIM">OSSDC SIM</a> - 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.
|
||
<ul>
|
||
<li><a href="https://github.com/OSSDC/OSSDC-SIM">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=fU_C38WEwGw">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://microsoft.github.io/AirSim">AirSim</a> - Unreal
|
||
Engine based simulator for drones and automotive. Compatible with ROS.
|
||
<ul>
|
||
<li><a href="https://github.com/microsoft/AirSim">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=gnz1X3UNM5Y">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://tier4.github.io/AWSIM">AWSIM</a> - Unity Engine
|
||
based simulator for automotive applications. Compatible with Autoware
|
||
and ROS 2.
|
||
<ul>
|
||
<li><a href="https://github.com/tier4/AWSIM">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a href="https://www.youtube.com/watch?v=FH7aBWDmSNA">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="related-awesome">Related awesome</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/Yochengliu/awesome-point-cloud-analysis#readme">Awesome
|
||
point cloud analysis <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/Kiloreux/awesome-robotics#readme">Awesome
|
||
robotics <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/jslee02/awesome-robotics-libraries#readme">Awesome
|
||
robotics libraries <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://github.com/fkromer/awesome-ros2#readme">Awesome ROS
|
||
2 <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a
|
||
href="https://github.com/owainlewis/awesome-artificial-intelligence#readme">Awesome
|
||
artificial intelligence <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/jbhuang0604/awesome-computer-vision#readme">Awesome
|
||
computer vision <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/josephmisiti/awesome-machine-learning#readme">Awesome
|
||
machine learning <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/ChristosChristofidis/awesome-deep-learning#readme">Awesome
|
||
deep learning <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a href="https://github.com/aikorea/awesome-rl/#readme">Awesome
|
||
reinforcement learning <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/youngguncho/awesome-slam-datasets#readme">Awesome
|
||
SLAM datasets <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/kitspace/awesome-electronics#readme">Awesome
|
||
electronics <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/jaredthecoder/awesome-vehicle-security#readme">Awesome
|
||
vehicle security and car hacking <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/Deephome/Awesome-LiDAR-Camera-Calibration">Awesome
|
||
LIDAR-Camera calibration <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/hogyun2/awesome-lidar-place-recognition">Awesome
|
||
LiDAR Place Recognition <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
<li><a
|
||
href="https://github.com/neng-wang/Awesome-LiDAR-MOS">Awesome-LiDAR-MOS
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
Moving Object Segmentation</li>
|
||
<li><a
|
||
href="https://github.com/sjtuyinjie/awesome-LiDAR-Visual-SLAM">Awesome-LiDAR-Visual-SLAM
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul>
|
||
<h2 id="others">Others</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/philipturner/ARHeadsetKit">ARHeadsetKit</a> -
|
||
Using $5 Google Cardboard to replicate Microsoft Hololens. Hosts the
|
||
source code for research on <a
|
||
href="https://github.com/philipturner/scene-color-reconstruction">scene
|
||
color reconstruction</a>.</li>
|
||
<li><a
|
||
href="https://github.com/marian42/pointcloudprinter">Pointcloudprinter
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- A tool to turn point cloud data from aerial lidar scans into solid
|
||
meshes for 3D printing.</li>
|
||
<li><a href="https://cloudcompare.org/">CloudCompare</a> - CloudCompare
|
||
is a free, cross-platform point cloud editor software.
|
||
<ul>
|
||
<li><a href="https://github.com/CloudCompare">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://github.com/keijiro/Pcx">Pcx <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- Point cloud importer/renderer for Unity.</li>
|
||
<li><a href="https://github.com/uhlik/bpy">Bpy <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- Point cloud importer/renderer/editor for Blender, Point Cloud
|
||
visualizer.</li>
|
||
<li><a
|
||
href="https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor">Semantic
|
||
Segmentation Editor <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- Point cloud and image semantic segmentation editor by Hitachi
|
||
Automotive And Industry Laboratory, point cloud annotator /
|
||
labeling.</li>
|
||
<li><a href="https://github.com/walzimmer/3d-bat">3D Bounding Box
|
||
Annotation Tool <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- 3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for
|
||
Full-Surround, Multi-Modal Data Streams, point cloud annotator /
|
||
labeling.
|
||
<ul>
|
||
<li><a href="https://arxiv.org/pdf/1905.00525.pdf">Paper <img
|
||
src="https://img.shields.io/badge/paper-blue?style=flat-square&logo=semanticscholar" /></a></li>
|
||
<li><a href="https://www.youtube.com/watch?v=gSGG4Lw8BSU">YouTube video
|
||
<img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a
|
||
href="https://github.com/SBCV/Blender-Addon-Photogrammetry-Importer">Photogrammetry
|
||
importer <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
- Blender addon to import reconstruction results of several
|
||
libraries.</li>
|
||
<li><a href="https://foxglove.dev/">Foxglove</a> - 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.
|
||
<ul>
|
||
<li><a href="https://github.com/foxglove/studio">GitHub repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a>
|
||
<img
|
||
src="https://img.shields.io/badge/ROS-2-34aec5?style=flat-square&logo=ros" /></li>
|
||
<li><a
|
||
href="https://www.youtube.com/channel/UCrIbrBxb9HBAnlhbx2QycsA">YouTube
|
||
channel <img
|
||
src="https://img.shields.io/badge/youtube-red?style=flat-square&logo=youtube" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://www.meshlab.net/">MeshLab</a> - MeshLab is an open
|
||
source, portable, and extensible system for the processing and editing
|
||
3D triangular meshes and pointcloud.
|
||
<ul>
|
||
<li><a href="https://github.com/cnr-isti-vclab/meshlab">GitHub
|
||
repository <img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
<li><a href="https://github.com/Geekgineer/CloudPeek">CloudPeek</a> is a
|
||
lightweight, c++ single-header, cross-platform point cloud viewer,
|
||
designed for simplicity and efficiency without relying on heavy external
|
||
libraries like PCL or Open3D.
|
||
<ul>
|
||
<li><a href="https://github.com/Geekgineer/CloudPeek">GitHub repository
|
||
<img
|
||
src="https://img.shields.io/badge/github-black?style=flat-square&logo=github" /></a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<p><a href="https://github.com/szenergy/awesome-lidar">lidar.md
|
||
Github</a></p>
|