RobustFieldAutonomyLab/LeGO-LOAM

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2753 stars · 1165 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LeGO-LOAM is a lightweight, ground-optimized lidar odometry and mapping (SLAM) system for ROS-compatible unmanned ground vehicles. It consumes point clouds from a horizontally mounted Velodyne VLP-16 plus optional IMU data and outputs real-time 6D pose estimation.

Use cases

• estimate the 6D pose of a ground robot in real time from lidar scans
• build a map of variable terrain using a VLP-16 lidar on a UGV
• perform ground-optimized lidar odometry with IMU fusion on a Clearpath Jackal
• adapt a SLAM pipeline to a new lidar sensor by tuning range image projection parameters
• run lightweight SLAM on resource-constrained ground vehicles

When to choose

• you need real-time lidar odometry and mapping on a ground vehicle with a visible ground plane
• you use a ROS-based UGV with a horizontally mounted VLP-16 and optional IMU
• you want a lightweight SLAM solution optimized for variable terrain

When to avoid

• your platform is aerial, handheld, or lacks a consistent ground plane
• you need a modern lidar-inertial odometry system with broader sensor support (consider LIO-SAM instead)
• you are not using ROS or cannot integrate C++ catkin packages

Facets

library · maturity maintenance

robotics computer-vision simulation robotics autonomous-vehicles cpp slam lidar-odometry mapping point-cloud velodyne imu gtsam ground-vehicle real-time pose-estimation algorithms linux ros

Member repositories

For agents

markdown · JSON · MCP: product_card(name="RobustFieldAutonomyLab/LeGO-LOAM")