LiDAR-MOS
Object Segmentation Library
This repository provides code and benchmarking tools for learning-based 3D LiDAR object segmentation using sequential data.
(LMNet) Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)
610 stars
15 watching
105 forks
Language: Python
last commit: over 3 years agoLinked from 1 awesome list
deep-learningdeep-neural-networksdynamic-slamlidarlidar-slammappingmotion-detectionmoving-object-segmentationpoint-cloudsegmentationslam
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A fast and robust algorithm to segment 3D point clouds generated by Velodyne sensors into objects. | 1,212 | |
| A C++ library for inference with LiDAR point clouds using the RangeNet++ algorithm and TensorRT | 315 | |
| A LiDAR odometry pipeline with a robust point-to-point ICP algorithm. | 1,621 | |
| A software framework that predicts the overlap and relative yaw angle of two 3D LiDAR scans to enable loop closing in SLAM systems | 671 | |
| Efficient LiDAR-based Semantic SLAM algorithm implementation | 926 | |
| Develops multiple object tracking and classification from LIDAR scans in real-time using point cloud processing and Kalman Filters | 803 | |
| Provides pre-trained deep learning model for semantic segmentation of 3D point clouds using SalsaNext architecture | 14 | |
| Package for 3D lidar slam using ndt/gicp registration and pose optimization in ROS2. | 560 | |
| Provides pre-trained deep learning models for object detection in point clouds using complex YOLOv4 architecture | 22 | |
| A ROS node implementing a probabilistic localization system using 3D LIDAR data and odometry | 504 | |
| Automatically calibrates LiDAR camera extrinsics based on RGB images and depth projections using deep neural networks. | 30 | |
| Develops an efficient network architecture for semantic segmentation of high-resolution remote sensing images. | 34 | |
| Research into efficient neural networks for semantic image segmentation in autonomous driving applications | 719 | |
| A PyTorch implementation of an online LiDAR point cloud segmentation neural network that provides near-real-time results | 382 | |
| Implementation of SqueezeSeg, a deep neural network model for segmenting 3D LiDAR point clouds into road objects and other features | 564 |