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)

Laser scan matcher ported to ROS2

ROS package for NDT-PSO, a 2D Laser scan matching algorithm for SLAM

[IROS'24] Globally localise your 2D LIDAR in a 2D map in no time

[IROS'22] Obtain robust odometry from your noisy panoramic 2D LIDAR

The Fourier Scan Matcher: a correspondenceless and closed-form matching algorithm for range scans of 2D panoramic LIDAR sensors

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

LiDAR Odometry with Scan Matching

Simple scan matcher based on the paper Real-Time Correlative Scan Matching by Olson 2009

Implemented the Iterative Closest Point (ICP) algorithm, and used it to estimate the rigid transformation that optimally aligns two 3D point clouds