There are 55 repositories under lidar topic.
A curated list of awesome data labeling tools
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
GAAS is an open-source program designed for fully autonomous VTOL(a.k.a flying cars) and drones. GAAS stands for Generalized Autonomy Aviation System.
Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.
3D LIDAR-based Graph SLAM
An "Iterative Closest Point" library for 2-D/3-D mapping in Robotics
ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"
The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
DJI Onboard SDK Official Repository
loam code noted in Chinese(loam中文注解版)
Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving
SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
LiDAR SLAM: Scan Context + LeGO-LOAM
Interactive Map Correction for 3D Graph SLAM
Object (e.g Pedestrian, vehicles) tracking by Extended Kalman Filter (EKF), with fused data from both lidar and radar sensors.
ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"
The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
Real-time 3D localization using a (velodyne) 3D LIDAR
Point cloud registration pipeline for robot localization and 3D perception
OverlapNet - Loop Closing for 3D LiDAR-based SLAM (chen2020rss)
[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
NaveGo: an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors analysis.
Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI)
Ground Segmentation from Lidar Point Clouds
SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021