OpenSimpleLidar - Open Hardware scanning laser rangefinder

•    C

Open Hardware scanning laser rangefinder. It is really cheap - its components cost less than $35.

Complex-YOLOv4-Pytorch - The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

•    Python

Use GIoU loss of rotated boxes for optimization.

awesome-robotic-tooling - Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace: https://freerobotics

To stop reinventing the wheel you need to know about the wheel. This list is an attempt to show the variety of open and free tools in software and hardware development, which are useful in professional robotic development. Your contribution is necessary to keep this list alive, increase the quality and to expand it. You can read more about it's origin and how you can participate in the contribution guide and related blog post. All new project entries will have a tweet from protontypes.

depth_clustering - :taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor

•    C++

This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velodyne sensors, i.e. 16, 32 and 64 beam ones. I recommend using a virtual environment in your catkin workspace (<catkin_ws> in this readme) and will assume that you have it set up throughout this readme. Please update your commands accordingly if needed. I will be using pipenv that you can install with pip.

LeGO-LOAM - LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

•    C++

An updated lidar-initial odometry package, LIO-SAM, has been open-sourced and available for testing. You can use the following commands to download and compile the package.

loam_velodyne - Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar

•    C++

Ask questions here. Issues #71 and #7 address this problem. The current known solution is to build the same version of PCL that you have on your system from source, and set the CMAKE_PREFIX_PATH accordingly so that catkin can find it. See this issue for more details.

A-LOAM - Advanced implementation of LOAM

•    C++

A-LOAM is an Advanced implementation of LOAM (J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), which uses Eigen and Ceres Solver to simplify code structure. This code is modified from LOAM and LOAM_NOTED. This code is clean and simple without complicated mathematical derivation and redundant operations. It is a good learning material for SLAM beginners. Follow Ceres Installation.

lidar_camera_calibration - ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

•    C++

The package is used to calibrate a LiDAR (config to support Hesai and Velodyne hardware) with a camera (works for both monocular and stereo). The package finds a rotation and translation that transform all the points in the LiDAR frame to the (monocular) camera frame. Please see Usage for a video tutorial. The lidar_camera_calibration/pointcloud_fusion provides a script to fuse point clouds obtained from two stereo cameras. Both of which were extrinsically calibrated using a LiDAR and lidar_camera_calibration. We show the accuracy of the proposed pipeline by fusing point clouds, with near perfection, from multiple cameras kept in various positions. See Fusion using lidar_camera_calibration for results of the point cloud fusion (videos).

superpoint_graph - Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

•    Python

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning https://arxiv.org/pdf/1904.02113. To switch to the stable branch with only SPG, switch to release.

lidar-bonnetal - Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving

•    Python

Semantic Segmentation of point clouds using range images. This code provides code to train and deploy Semantic Segmentation of LiDAR scans, using range images as intermediate representation. The training pipeline can be found in /train. We will open-source the deployment pipeline soon.

semantic_suma - SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

•    C++

This repository contains the implementation of SuMa++, which generates semantic maps only using three-dimensional laser range scans. Developed by Xieyuanli Chen and Jens Behley.

pptk - The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds

•    C++

Copyright (C) 2011-2018 HERE Europe B.V. The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

dynamic_robot_localization - Point cloud registration pipeline for robot localization and 3D perception

•    C++

The dynamic_robot_localization is a ROS package that offers 3 DoF and 6 DoF localization using PCL and allows dynamic map update using OctoMap. It's a modular localization pipeline, that can be configured using yaml files (detailed configuration layout available in drl_configs.yaml and examples of configurations available in guardian_config and dynamic_robot_localization_tests). Even though this package was developed for robot self-localization and mapping, it was implemented as a generic, configurable and extensible point cloud matching library, allowing its usage in related problems such as estimation of the 6 DoF pose of an object and 3D object scanning.

interactive_slam - Interactive Map Correction for 3D Graph SLAM

•    C++

This package is built on top of the ROS ecosystem. You can start building a map with a pose graph constructed by hdl_graph_slam or a customized LeGO-LOAM, or odometry data generated by any ROS package. This package has been tested on Ubuntu 18.04 & ROS melodic or later.

OverlapNet - OverlapNet - Loop Closing for 3D LiDAR-based SLAM (chen2020rss)

•    Python

This repo contains the code for our RSS2020 paper, OverlapNet. OverlapNet is modified Siamese Network that predicts the overlap and relative yaw angle of a pair of range images generated by 3D LiDAR scans.

SC-LeGO-LOAM - LiDAR SLAM: Scan Context + LeGO-LOAM

•    C++

LiDAR SLAM: Scan Context + LeGO-LOAM

Livox-SDK - Drivers for receiving LiDAR data and more

•    C++

Livox SDK is the software development kit designed for all Livox products. It is developed based on C/C++ following Livox SDK Communication Protocol, and provides easy-to-use C style API. With Livox SDK, users can quickly connect to Livox products and receive point cloud data. Livox SDK consists of Livox SDK communication protocol, Livox SDK core, Livox SDK API, Linux sample, and ROS demo.

ILCC - Intensity-based_Lidar_Camera_Calibration

•    Python

Make a folder for example named as DATA and make the image and point cloud folders DATA/img and DATA/pcd respectively. Put panoramic images into DATA/img and point cloud files into DATA/pcd. The files should be named like 00XX.png or 00XX.csv.

lopocs - Light OpenSource PointCloud Server

•    Python

LOPoCS is a point cloud server written in Python, allowing to load Point Cloud from a PostgreSQL database thanks to the pgpointcloud extension. Note that LOPoCS is currently the only 3DTiles server able to stream data from pgpointcloud. This is possible thanks to the python module py3dtiles.

lidR - R package for airborne LiDAR data manipulation and visualisation for forestry application

•    R

The lidR package provides functions to read and write .las and .laz files, plot point clouds, compute metrics using an area-based approach, compute digital canopy models, thin lidar data, manage a catalog of datasets, automatically extract ground inventories, process a set of tiles using multicore processing, individual tree segmentation, classify data from geographic data, and provides other tools to manipulate LiDAR data in a research and development context. Development of the lidR package between 2015 and 2018 was made possible thanks to the financial support of the AWARE project (NSERC CRDPJ 462973-14); grantee Prof Nicholas Coops.