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

[IROS'24] 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] Obtain 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.