TixiaoShan/LIO-SAM

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4895 stars · 1528 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LIO-SAM is a real-time tightly-coupled lidar-inertial odometry and mapping package built on factor graph optimization with GTSAM. It fuses lidar and IMU data to produce high-accuracy 3D odometry and mapping, running up to 10x faster than real time.

Use cases

• estimate robot pose from lidar and IMU data
• build 3D maps with a Velodyne or Ouster lidar
• run real-time SLAM on a mobile robot or handheld device
• fuse GPS factors with lidar odometry
• localize a robot in a previously mapped environment

When to choose

• you need accurate real-time lidar-inertial odometry in ROS
• you have a supported lidar (Velodyne, Ouster, Livox) with a 9-axis IMU
• you want a well-cited, widely used SLAM baseline for research or deployment

When to avoid

• you have no IMU or only low-rate IMU data
• you need visual or visual-inertial SLAM with cameras
• you are not using ROS and don't want to adapt the code

Facets

library · maturity stable

robotics simulation computer-vision math robotics autonomous-vehicles cpp slam lidar-odometry lidar-inertial factor-graph-optimization gtsam ros 3d-mapping velodyne ouster algorithms linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="TixiaoShan/LIO-SAM")