Efficient and parallel algorithms for point cloud registration [C++, Python]
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Updated
May 28, 2024 - C++
Efficient and parallel algorithms for point cloud registration [C++, Python]
Point cloud registration pipeline for robot localization and 3D perception
The Fourier Scan Matcher: a correspondenceless and closed-form matching algorithm for 2D panoramic LIDAR sensors
K-Closest Points and Maximum Clique Pruning for Efficient and Effective 3-D Laser Scan Matching (RA-L 2022)
[ROS package] Lidar odometry from panoramic 2D range scans. Method: scan-matching without using correspondences, based on properties of the Discrete Fourier Transform
A collection of GICP-based fast point cloud registration algorithms
Localise your 2D LIDAR in a 2D map ex novo in no time
Acquire robust odometry from your noisy panoramic 2D LIDAR sensor
Multi-threaded and SSE friendly NDT algorithm
An implementation of Simultaneous Localization and Mapping.
Laser scan matcher ported to ROS2
Implemented the Iterative Closest Point (ICP) algorithm, and used it to estimate the rigid transformation that optimally aligns two 3D point clouds
ROS package for NDT-PSO, a 2D Laser scan matching algorithm for SLAM
Simple 2D point-to-point scan matcher implemented in Python. Works with ROS1.
This repository contains solution for SLAM lectures taught by Claus Brenner on YouTube.
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