Faster-LIO is a lightweight, tightly coupled LiDAR-inertial odometry system for pose tracking and point-cloud mapping. It builds on FastLIO2 while replacing key spatial operations with parallel sparse incremental voxels. The design targets high update rates for both solid-state and spinning LiDAR sensors. It fuses LiDAR measurements with inertial data through an iterative Kalman-filter-based estimation pipeline. The software supports online ROS operation as well as offline processing of recorded bag files. Users can compile it with CMake or catkin, run it through Docker, visualize output in RViz, and save trajectories and point clouds. Configuration files support multiple sensor datasets, while selectable voxel implementations allow performance and stability tuning.
Features
- Tightly coupled LiDAR and inertial estimation
- Parallel sparse incremental voxel mapping
- Solid-state and spinning LiDAR support
- Online ROS and offline bag processing
- CMake, catkin, and Docker workflows
- RViz visualization and output file generation