hyye/lio-mapping

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1028 stars · 324 forks observed · 2026-08-28

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

A C++/ROS implementation of tightly coupled 3D lidar-inertial odometry and mapping (LIO-mapping), published at ICRA 2019. It jointly optimizes lidar and IMU measurements to estimate sensor poses at IMU update rate and refine them against a global map.

Use cases

• estimate robot trajectory from lidar and IMU data
• build 3D maps from spinning lidar point clouds
• run lidar-inertial odometry on rosbag recordings
• reproduce ICRA 2019 lidar-inertial fusion results
• compare SLAM odometry methods against LOAM and VINS-mono baselines

When to choose

• you need tightly coupled lidar-IMU fusion with high-rate pose estimation
• you work in ROS with Velodyne-style 3D lidar and want a research-grade odometry pipeline
• you want a reference implementation of the LIO-mapping paper for academic comparison

When to avoid

• you need a maintained project with recent updates or active community support
• you want visual-inertial or lidar-visual-inertial SLAM rather than lidar-IMU only
• you are not using ROS or Linux, since the build depends on ROS, Ceres, PCL, and OpenCV

Facets

library · maturity maintenance

simulation computer-vision machine-learning robotics autonomous-vehicles cpp slam lidar sensor-fusion imu odometry mapping point-cloud ceres-solver ros icra-2019 algorithms linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="hyye/lio-mapping")