A collection of GICP-based fast point cloud registration algorithms
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Updated
Jul 29, 2024 - C++
A collection of GICP-based fast point cloud registration algorithms
Point cloud registration pipeline for robot localization and 3D perception
Multi-threaded and SSE friendly NDT algorithm
Efficient and parallel algorithms for point cloud registration [C++, Python]
K-Closest Points and Maximum Clique Pruning for Efficient and Effective 3-D Laser Scan Matching (RA-L 2022)
ROS package for NDT-PSO, a 2D Laser scan matching algorithm for SLAM
Laser scan matcher ported to ROS2
Globally localise your 2D LIDAR in a 2D map in no time
The Fourier Scan Matcher: a correspondenceless and closed-form matching algorithm for 2D panoramic LIDAR sensors
[IROS'22] Acquire robust odometry from your noisy panoramic 2D LIDAR
This repository contains solution for SLAM lectures taught by Claus Brenner on YouTube.
Simple 2D point-to-point scan matcher implemented in Python. Works with ROS1.
[ROS package] Lidar odometry from panoramic 2D range scans. Method: scan-matching without using correspondences, based on properties of the Discrete Fourier Transform
Implemented the Iterative Closest Point (ICP) algorithm, and used it to estimate the rigid transformation that optimally aligns two 3D point clouds
An implementation of Simultaneous Localization and Mapping.
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