LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

Advanced implementation of LOAM

Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.

A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR

Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021

loam code noted in Chinese（loam中文注解版）

Intensity Scan Context based full SLAM implementation for autonomous driving. ICRA 2020

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.

CMU 16-833 "Robot Localization and Mapping" Course Project

SLAM package using NDT registration library of Autoware with loop-closure detection (odometry based) referenced from lego_loam.

open source slam system notes

Easy description to run and evaluate Lego-LOAM with KITTI-data

A CUDA reimplementation of the line/plane odometry of LIO-SAM. A point cloud hash map (inspired by iVox of Faster-LIO) on GPU is used to accelerate 5-neighbour KNN search.

This dataset is captured using a Velodyne VLP-16, which is mounted on an UGV - Clearpath Jackal, on Stevens Institute of Technology campus

LIO-SAM-6AXIS with intensity image loop optimization

Bag files captured using a Clearpath Jackal Robot, which is equipped with a Velodyne VLP-16 and low-end IMU sensor. The published point cloud topic is \velodyne_points. The published IMU data topic is \imu\data.

Advance-LeGO-LOAM

Python implementation of LOAM (Lidar Odometry and Mapping) for rapid prototyping or educational purpose