rhetenor/awesome-awesomeness
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
55bed3b4a1eed31ec662fb9c9b8884a48aa62ff8
awesome-awesomeness/html/lidar.html
Jonas Zeunert 2d63fe63cd Restructure with Makefile + add html output
2024-04-20 19:22:54 +02:00

861 lines
34 KiB
HTML
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
<h1 id="awesome-lidar-awesome">Awesome LIDAR <a
href="https://awesome.re"><img src="https://awesome.re/badge.svg"
alt="Awesome" /></a></h1>
<p><img src="img/lidar.svg" align="right" width="100"></p>
<blockquote>
<p>A curated list of awesome LIDAR sensors and its applications.</p>
</blockquote>
<p><a href="https://en.wikipedia.org/wiki/Lidar">LIDAR</a> 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.</p>
<p>Contributions are welcome! Please <a href="contributing.md">check
out</a> our guidelines.</p>
<h2 id="contents">Contents</h2>
<ul>
<li><a href="#awesome-lidar-">Awesome LIDAR</a>
<ul>
<li><a href="#contents">Contents</a></li>
<li><a href="#conventions">Conventions</a></li>
<li><a href="#manufacturers">Manufacturers</a></li>
<li><a href="#datasets">Datasets</a></li>
<li><a href="#libraries">Libraries</a></li>
<li><a href="#frameworks">Frameworks</a></li>
<li><a href="#algorithms">Algorithms</a>
<ul>
<li><a href="#basic-matching-algorithms">Basic matching
algorithms</a></li>
<li><a href="#semantic-segmentation">Semantic segmentation</a></li>
<li><a href="#ground-segmentation">Ground segmentation</a></li>
<li><a
href="#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</a></li>
<li><a href="#object-detection-and-object-tracking">Object detection and
object tracking</a></li>
</ul></li>
<li><a href="#simulators">Simulators</a></li>
<li><a href="#related-awesome">Related awesome</a></li>
<li><a href="#others">Others</a></li>
</ul></li>
</ul>
<h2 id="conventions">Conventions</h2>
<ul>
<li>Any list item with an OctoCat :octocat: has a GitHub repo or
organization</li>
<li>Any list item with a RedCircle :red_circle: has YouTube videos or
channel</li>
<li>Any list item with a Paper :newspaper: has a scientific paper or
detailed description</li>
</ul>
<h2 id="manufacturers">Manufacturers</h2>
<ul>
<li><a href="https://velodynelidar.com/">Velodyne</a> - Ouster and
Velodyne announced the successful completion of their <em>merger</em> of
equals, effective February 10, 2023. Velodyne was a mechanical and
solid-state LIDAR manufacturer. The headquarter is in San Jose,
California, USA.
<ul>
<li><a href="https://www.youtube.com/user/VelodyneLiDAR">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/ros-drivers/velodyne">ROS driver
:octocat:</a></li>
<li><a href="https://github.com/valgur/velodyne_decoder">C++/Python
library :octocat:</a></li>
</ul></li>
<li><a href="https://ouster.com/">Ouster</a> - LIDAR manufacturer,
specializing in digital-spinning LiDARs. Ouster is headquartered in San
Francisco, USA.
<ul>
<li><a href="https://www.youtube.com/c/Ouster-lidar">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/ouster-lidar">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.livoxtech.com/">Livox</a> - LIDAR manufacturer.
<ul>
<li><a
href="https://www.youtube.com/channel/UCnLpB5QxlQUexi40vM12mNQ">YouTube
channel :red_circle:</a></li>
<li><a href="https://github.com/Livox-SDK">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.sick.com/ag/en/">SICK</a> - Sensor and
automation manufacturer, the headquarter is located in Waldkirch,
Germany.
<ul>
<li><a href="https://www.youtube.com/user/SICKSensors">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/SICKAG">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.hokuyo-aut.jp/">Hokuyo</a> - Sensor and
automation manufacturer, headquartered in Osaka, Japan.
<ul>
<li><a
href="https://www.youtube.com/channel/UCYzJXC82IEy-h-io2REin5g">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="http://autonomousdriving.pioneer/en/3d-lidar/">Pioneer</a>
- LIDAR manufacturer, specializing in MEMS mirror-based raster scanning
LiDARs (3D-LiDAR). Pioneer is headquartered in Tokyo, Japan.
<ul>
<li><a href="https://www.youtube.com/user/PioneerCorporationPR">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="https://www.luminartech.com/">Luminar</a> - LIDAR
manufacturer focusing on compact, auto-grade sensors. Luminar is
headquartered Palo Alto, California, USA.
<ul>
<li><a href="https://vimeo.com/luminartech">Vimeo channel
:red_circle:</a></li>
<li><a href="https://github.com/luminartech">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.hesaitech.com/">Hesai</a> - Hesai Technology is
a LIDAR manufacturer, founded in Shanghai, China.
<ul>
<li><a
href="https://www.youtube.com/channel/UCG2_ffm6sdMsK-FX8yOLNYQ/videos">YouTube
channel :red_circle:</a></li>
<li><a href="https://github.com/HesaiTechnology">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="http://www.robosense.ai/">Robosense</a> - RoboSense (Suteng
Innovation Technology Co., Ltd.) is a LIDAR sensor, AI algorithm and IC
chipset maufactuirer based in Shenzhen and Beijing (China).
<ul>
<li><a
href="https://www.youtube.com/channel/UCYCK8j678N6d_ayWE_8F3rQ">YouTube
channel :red_circle:</a></li>
<li><a href="https://github.com/RoboSense-LiDAR">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.lslidar.com/">LSLIDAR</a> - LSLiDAR (Leishen
Intelligent System Co., Ltd.) is a LIDAR sensor manufacturer and
complete solution provider based in Shenzhen, China.
<ul>
<li><a href="https://www.youtube.com/@lslidar2015">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/Lslidar">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.ibeo-as.com/">Ibeo</a> - Ibeo Automotive
Systems GmbH is an automotive industry / environmental detection
laserscanner / LIDAR manufacturer, based in Hamburg, Germany.
<ul>
<li><a href="https://www.youtube.com/c/IbeoAutomotive/">YouTube channel
:red_circle:</a></li>
</ul></li>
<li><a href="https://innoviz.tech/">Innoviz</a> - Innoviz technologies /
specializes in solid-state LIDARs.
<ul>
<li><a
href="https://www.youtube.com/channel/UCVc1KFsu2eb20M8pKFwGiFQ">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="https://quanergy.com/">Quanenergy</a> - 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.
<ul>
<li><a href="https://www.youtube.com/c/QuanergySystems">YouTube channel
:red_circle:</a></li>
</ul></li>
<li><a href="https://www.cepton.com/index.html">Cepton</a> - 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.
<ul>
<li><a
href="https://www.youtube.com/channel/UCUgkBZZ1UWWkkXJ5zD6o8QQ">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="https://www.blickfeld.com/">Blickfeld</a> - Blickfeld is a
solid-state LIDAR manufacturer for autonomous mobility and IoT, based in
München, Germany.
<ul>
<li><a href="https://www.youtube.com/c/BlickfeldLiDAR">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/Blickfeld">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.neuvition.com/">Neuvition</a> - Neuvition is a
solid-state LIDAR manufacturer based in Wujiang, China.
<ul>
<li><a
href="https://www.youtube.com/channel/UClFjlekWJo4T5bfzxX0ZW3A">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="https://www.aeva.com/">Aeva</a> - 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.
<ul>
<li><a href="https://www.youtube.com/c/AevaInc">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/aevainc">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.xenomatix.com/">XenomatiX</a> - XenomatiX
offers true solid-state lidar sensors based on a multi-beam lasers
concept. XenomatiX is headquartered in Leuven, Belgium.
<ul>
<li><a
href="https://www.youtube.com/@XenomatiXTruesolidstatelidar">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="https://microvision.com/">MicroVision</a> - A pioneer in
MEMS-based laser beam scanning technology, the main focus is on building
Automotive grade Lidar sensors, located in Hamburg, Germany.
<ul>
<li><a href="https://www.youtube.com/user/mvisvideo">YouTube channel
:red_circle:</a></li>
<li><a href="https://github.com/MicroVision-Inc">GitHub organization
:octocat:</a></li>
</ul></li>
<li><a href="https://www.preact-tech.com/">PreAct</a> - PreAct’s mission
is to make life safer and more efficient for the automotive industry and
beyond. The headquarter is located in Portland, Oregon, USA.
<ul>
<li><a href="https://www.youtube.com/@PreActTechnologies">YouTube
channel :red_circle:</a></li>
</ul></li>
</ul>
<h2 id="datasets">Datasets</h2>
<ul>
<li><a href="https://avdata.ford.com/">Ford Dataset</a> - 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.
<ul>
<li><a href="https://arxiv.org/pdf/2003.07969.pdf">Paper
:newspaper:</a></li>
<li><a href="https://github.com/Ford/AVData">GitHub repository
:octocat:</a></li>
</ul></li>
<li><a href="https://www.a2d2.audi">Audi A2D2 Dataset</a> - The dataset
features 2D semantic segmentation, 3D point clouds, 3D bounding boxes,
and vehicle bus data.
<ul>
<li><a
href="https://www.a2d2.audi/content/dam/a2d2/dataset/a2d2-audi-autonomous-driving-dataset.pdf">Paper
:newspaper:</a></li>
</ul></li>
<li><a href="https://waymo.com/open/">Waymo Open Dataset</a> - The
dataset contains independently-generated labels for lidar and camera
data, not simply projections.</li>
<li><a href="https://robotcar-dataset.robots.ox.ac.uk/">Oxford
RobotCar</a> - The Oxford RobotCar Dataset contains over 100 repetitions
of a consistent route through Oxford, UK, captured over a period of over
a year.
<ul>
<li><a
href="https://www.youtube.com/c/ORIOxfordRoboticsInstitute">YouTube
channel :red_circle:</a></li>
<li><a
href="https://robotcar-dataset.robots.ox.ac.uk/images/RCD_RTK.pdf">Paper
:newspaper:</a></li>
</ul></li>
<li><a href="https://epan-utbm.github.io/utbm_robocar_dataset/">EU
Long-term Dataset</a> - 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.</li>
<li><a href="https://www.nuscenes.org/">NuScenes</a> - Public
large-scale dataset for autonomous driving.
<ul>
<li><a href="https://arxiv.org/pdf/1903.11027.pdf">Paper
:newspaper:</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.</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
:red_circle:</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
:newspaper:</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
:newspaper:</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 &amp; 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
:newspaper:</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
:newspaper:</a></li>
</ul></li>
<li><a href="https://github.com/Robotics-BUT/Brno-Urban-Dataset">Brno
Urban Dataset :octocat:</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
:newspaper:</a></li>
<li><a href="https://www.youtube.com/watch?v=wDFePIViwqY">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://www.argoverse.org/">Argoverse :octocat:</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
:newspaper:</a></li>
<li><a href="https://www.youtube.com/watch?v=DM8jWfi69zM">YouTube video
:red_circle:</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
:octocat:</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
:octocat:</a></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
:octocat:</a></li>
<li><a
href="https://www.youtube.com/channel/UCRJBlASPfPBtPXJSPffJV-w">YouTube
channel :red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/1903.02428.pdf">PyTorch Geometric
:newspaper:</a> - A geometric deep learning extension library for
PyTorch.
<ul>
<li><a href="https://github.com/rusty1s/pytorch_geometric">GitHub
repository :octocat:</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 :octocat:</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 :octocat:</a></li>
<li><a href="https://arxiv.org/pdf/1911.05063.pdf">Paper
:newspaper:</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
:octocat:</a></li>
<li><a href="https://joss.theoj.org/papers/10.21105/joss.01450">Paper
:newspaper:</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
:octocat:</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 :octocat:</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://gitlab.com/autowarefoundation/autoware.ai">GitLab
repository :octocat:</a></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
:newspaper:</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
:octocat:</a></li>
<li><a href="https://www.youtube.com/c/ApolloAuto">YouTube channel
:red_circle:</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) :red_circle:</a> - The must-have algorithm for
feature matching applications (ICP).
<ul>
<li><a href="https://github.com/pglira/simpleICP">GitHub repository
:octocat:</a> - simpleICP C++ /Julia / Matlab / Octave / Python
implementation.</li>
<li><a href="https://github.com/ethz-asl/libpointmatcher">GitHub
repository :octocat:</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
:newspaper:</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 :red_circle:</a> - More recent
massively-parallel approach to feature matching (NDT).</li>
<li><a href="https://www.youtube.com/watch?v=kMMH8rA1ggI">KISS-ICP
:red_circle:</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
:octocat:</a></li>
<li><a href="https://arxiv.org/pdf/2209.15397.pdf">Paper
:newspaper:</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++
:newspaper:</a> - Fast and Accurate LiDAR Sematnic Segmentation with
fully convolutional network.
<ul>
<li><a href="https://github.com/PRBonn/rangenet_lib">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=uo3ZuLuFAzk">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/2003.14032.pdf">PolarNet
:newspaper:</a> - An Improved Grid Representation for Online LiDAR Point
Clouds Semantic Segmentation.
<ul>
<li><a href="https://github.com/edwardzhou130/PolarSeg">GitHub
repository :octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=iIhttRSMqjE">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/1711.08488.pdf">Frustum PointNets
:newspaper:</a> - Frustum PointNets for 3D Object Detection from RGB-D
Data.
<ul>
<li><a href="https://github.com/charlesq34/frustum-pointnets">GitHub
repository :octocat:</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
:newspaper:</a></li>
<li><a href="http://ltriess.github.io/scan-semseg">GitHub repository
:octocat:</a></li>
</ul></li>
<li><a
href="https://www.ipb.uni-bonn.de/pdfs/chen2021ral-iros.pdf">LIDAR-MOS
:newspaper:</a> - Moving Object Segmentation in 3D LIDAR Data
<ul>
<li><a href="https://github.com/PRBonn/LiDAR-MOS">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=NHvsYhk4dhw">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/1711.09869.pdf">SuperPoint Graph
:newspaper:</a>- Large-scale Point Cloud Semantic Segmentation with
Superpoint Graphs
<ul>
<li><a href="https://github.com/PRBonn/LiDAR-MOS">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=Ijr3kGSU_tU">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/1911.11236.pdf">RandLA-Net
:newspaper:</a> - Efficient Semantic Segmentation of Large-Scale Point
Clouds
<ul>
<li><a href="https://github.com/QingyongHu/RandLA-Net">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=Ar3eY_lwzMk">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/2108.13757.pdf">Automatic labelling
:newspaper:</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 :octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=qMj_WM6D0vI">YouTube video
:red_circle:</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
:octocat:</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
:red_circle:</a></li>
</ul></li>
<li><a
href="https://ieeexplore.ieee.org/abstract/document/5548059">LineFit
Graph :newspaper:</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 :octocat:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/2108.05560.pdf">Patchwork
:newspaper:</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
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=rclqeDi4gow">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/2207.11919.pdf">Patchwork++
:newspaper:</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 :octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=fogCM159GRk">YouTube video
:red_circle:</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
:red_circle:</a> - LOAM: Lidar Odometry and Mapping in Real-time.</li>
<li><a
href="https://github.com/RobustFieldAutonomyLab/LeGO-LOAM">LeGO-LOAM
:octocat:</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
:red_circle:</a></li>
</ul></li>
<li><a
href="https://github.com/cartographer-project/cartographer">Cartographer
:octocat:</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.
<ul>
<li><a href="https://www.youtube.com/watch?v=29Knm-phAyI">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a
href="http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf">SuMa++
:newspaper:</a> - LiDAR-based Semantic SLAM.
<ul>
<li><a href="https://github.com/PRBonn/semantic_suma/">GitHub repository
:octocat:</a></li>
<li><a href="https://youtu.be/uo3ZuLuFAzk">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a
href="http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020rss.pdf">OverlapNet
:newspaper:</a> - Loop Closing for LiDAR-based SLAM.
<ul>
<li><a href="https://github.com/PRBonn/OverlapNet">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=YTfliBco6aw">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a href="https://arxiv.org/pdf/2007.00258.pdf">LIO-SAM
:newspaper:</a> - Tightly-coupled Lidar Inertial Odometry via Smoothing
and Mapping.
<ul>
<li><a href="https://github.com/TixiaoShan/LIO-SAM">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=A0H8CoORZJU">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a
href="http://ras.papercept.net/images/temp/IROS/files/0855.pdf">Removert
:newspaper:</a> - Remove, then Revert: Static Point cloud Map
Construction using Multiresolution Range Images.
<ul>
<li><a href="https://github.com/irapkaist/removert">GitHub repository
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=M9PEGi5fAq8">YouTube video
:red_circle:</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 :newspaper:</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
:red_circle:</a></li>
<li><a href="https://github.com/peiyunh/opcseg">GitHub repository
:octocat:</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 :newspaper:</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
:red_circle:</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 :newspaper:</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
:red_circle:</a></li>
<li><a href="https://github.com/peiyunh/WYSIWYG">GitHub repository
:octocat:</a></li>
</ul></li>
<li><a href="https://doi.org/10.3390/s22010194">urban_road_filter
:newspaper:</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 :octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=T2qi4pldR-E">YouTube video
:red_circle:</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 :red_circle:</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
:octocat:</a></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
:octocat:</a></li>
<li><a
href="https://www.youtube.com/channel/UC1llP9ekCwt8nEJzMJBQekg">YouTube
channel :red_circle:</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
:octocat:</a></li>
<li><a href="https://www.youtube.com/c/LGSVLSimulator">YouTube channel
:red_circle:</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
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=fU_C38WEwGw">YouTube video
:red_circle:</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
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=gnz1X3UNM5Y">YouTube video
:red_circle:</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
:octocat:</a></li>
<li><a href="https://www.youtube.com/watch?v=FH7aBWDmSNA">YouTube video
:red_circle:</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 :octocat:</a></li>
<li><a
href="https://github.com/Kiloreux/awesome-robotics#readme">Awesome
robotics :octocat:</a></li>
<li><a
href="https://github.com/jslee02/awesome-robotics-libraries#readme">Awesome
robotics libraries :octocat:</a></li>
<li><a href="https://github.com/fkromer/awesome-ros2#readme">Awesome ROS
2 :octocat:</a></li>
<li><a
href="https://github.com/owainlewis/awesome-artificial-intelligence#readme">Awesome
artificial intelligence :octocat:</a></li>
<li><a
href="https://github.com/jbhuang0604/awesome-computer-vision#readme">Awesome
computer vision :octocat:</a></li>
<li><a
href="https://github.com/josephmisiti/awesome-machine-learning#readme">Awesome
machine learning :octocat:</a></li>
<li><a
href="https://github.com/ChristosChristofidis/awesome-deep-learning#readme">Awesome
deep learning :octocat:</a></li>
<li><a href="https://github.com/aikorea/awesome-rl/#readme">Awesome
reinforcement learning :octocat:</a></li>
<li><a
href="https://github.com/youngguncho/awesome-slam-datasets#readme">Awesome
SLAM datasets :octocat:</a></li>
<li><a
href="https://github.com/kitspace/awesome-electronics#readme">Awesome
electronics :octocat:</a></li>
<li><a
href="https://github.com/jaredthecoder/awesome-vehicle-security#readme">Awesome
vehicle security and car hacking :octocat:</a></li>
<li><a
href="https://github.com/Deephome/Awesome-LiDAR-Camera-Calibration">Awesome
LIDAR-Camera calibration :octocat:</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
:octocat:</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
:octocat:</a></li>
</ul></li>
<li><a href="https://github.com/keijiro/Pcx">Pcx :octocat:</a> - Point
cloud importer/renderer for Unity.</li>
<li><a href="https://github.com/uhlik/bpy">Bpy :octocat:</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 :octocat:</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 :octocat:</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
:newspaper:</a></li>
<li><a href="https://www.youtube.com/watch?v=gSGG4Lw8BSU">YouTube video
:red_circle:</a></li>
</ul></li>
<li><a
href="https://github.com/SBCV/Blender-Addon-Photogrammetry-Importer">Photogrammetry
importer :octocat:</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
:octocat:</a></li>
<li><a
href="https://www.youtube.com/channel/UCrIbrBxb9HBAnlhbx2QycsA">YouTube
channel :red_circle:</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 :octocat:</a></li>
</ul></li>
</ul>
Reference in New Issue View Git Blame Copy Permalink