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OverlapNet - Loop Closing for 3D LiDAR-based SLAM (chen2020rss)
The main features of prbonn/overlapnet are: SLAM and Odometry, Point Cloud Processing.
Open-source alternatives to prbonn/overlapnet include: tixiaoshan/lio-sam — LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph… irapkaist/removert — Remove then revert (IROS 2020). michaelgrupp/evo — evo is a Python framework for the evaluation of SLAM algorithms, robot odometry, and trajectory data. It serves as an… cgal/cgal — CGAL is a software library that provides a comprehensive collection of computational geometry algorithms and data… hku-mars/fast_lio — FAST_LIO is a real-time SLAM system and LiDAR-inertial odometry package designed for simultaneous localization and… hku-mars/fast-livo2 — FAST-LIVO2 is a LiDAR-inertial odometry framework and factor-graph SLAM implementation designed for real-time robot…
LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto
evo is a Python framework for the evaluation of SLAM algorithms, robot odometry, and trajectory data. It serves as an analysis library for measuring drift and precision by calculating absolute and relative pose errors between estimated paths and ground truth references. The project provides a geometric alignment framework to correct rotation, translation, and scale between spatial trajectories, ensuring consistent error measurement. It includes specialized tools for odometry drift analysis and the processing of robotics data, including the ability to extract trajectory information from ROS ba
FAST-LIVO2 is a LiDAR-inertial odometry framework and factor-graph SLAM implementation designed for real-time robot localization and 3D mapping. It functions as a multi-sensor fusion pipeline and state estimator that integrates LiDAR, inertial, and camera inputs to track a robot's position and orientation. The system employs a tightly-coupled sensor fusion approach to maintain stable navigation, particularly in degraded environments. It utilizes a voxel-based 3D mapping tool to organize point clouds into volumetric grids, which optimizes memory usage and search speed during spatial reconstruc
