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The main features of asig-x/resple are: SLAM and Odometry.
Projects with overlapping indexed features include: michaelgrupp/evo — evo is a Python framework for the evaluation of SLAM algorithms, robot odometry, and trajectory data. It serves as an… hku-mars/fast-livo2 — FAST-LIVO2 is a LiDAR-inertial odometry framework and factor-graph SLAM implementation designed for real-time robot… irapkaist/removert — Remove then revert (IROS 2020). molaorg/mola. prbonn/kiss-slam — KISS-SLAM is a simple, robust, and accurate 3D LiDAR SLAM system that just works. prbonn/overlapnet — OverlapNet - Loop Closing for 3D LiDAR-based SLAM (chen2020rss).
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
This code is a fork from LeGO-LOAM-BOR to migrate LeGO-LOAM algorithm to ROS2.
