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

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

SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021. Ported to ROS2 Humble

Rust+Python driver for the Delta2 LiDAR, manufactured by 3irobotix

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

A real-time LiDAR SLAM package that integrates FLOAM and ScanContext.

A real-time LiDAR SLAM package that integrates TLOAM and ScanContext.

A re-implementation of A-LOAM with more modularized design and clearer logic

LOAM based out of LeGO-LOAM

LIDAR(Livox Horizon) point cloud preprocessing, including point cloud filtering and point cloud feature extraction (edge points and planar points)

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

LiLo: Lite 3D-Lidar Odometry

css_loam_velodyne

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

Laser Odometry and Localization

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.

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

A package transplanted from LOAM（Zhang Ji）