⚡Super-LIO

Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy

This work has been accepted to IEEE Robotics and Automation Letters (RA-L 2026).

     

Overview

Key Features: Efficient · Robust · Cross-Platform Compatible · Supports Both ROS1/ROS2 Versions

Super-LIO is a robust and efficient LiDAR–Inertial Odometry (LIO) system designed for real-time and large-scale autonomous navigation. It introduces a compact and structured mapping strategy that enables predictable correspondence search and stable state estimation. The system is validated through extensive real-world experiments and comparisons with state-of-the-art methods, which demonstrates that Super-LIO not only achieves excellent accuracy but also maintains lower resource consumption and realizes a nearly 1.2–4× higher real-time processing speed⚡.

Contributors: Liansheng Wang, Xinke Zhang, Chenhui Li, Dongjiao He, Yihan pan, Jianjun Yi.

Quickly Run

For ROS1 Users: Please switch to the ros1 branch and follow the instructions at ros1 branch

Requirements

Ubuntu 24(22).04 · C++20 · ROS Jazzy(Humble) · Eigen · PCL

Dependencies

glog · TBB

sudo apt install libgoogle-glog-dev libtbb-dev

Build & Run

git clone https://github.com/Liansheng-Wang/Super-LIO.git
cd Super-LIO
colcon build

source install/setup.bash
ros2 launch super_lio Livox_mid360.py

🔁 Relocalization Mode

Super-LIO supports relocalization using a pre-built map, allowing the system to resume localization from a saved map without restarting the mapping process. This mode is useful for long-term deployment, repeated missions, or recovery after tracking loss.

Before running relocalization, please make sure that:

• A map has been previously saved to disk.

cd PATH_2_Super-LIO
source install/setup.bash
ros2 launch super_lio relocation.py

Datasets

Super-LIO is evaluated on multiple real-world datasets covering diverse environments, including indoor, outdoor, and large-scale scenes.

TODO: Dataset download links and detailed descriptions will be provided in the future.

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Publications

If your like our projects, please cite us and support us with a star 🌟. We kindly recommend to cite our paper if you find this library useful:

@article{wang2026superlio,
  title   = {Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy},
  author  = {Wang, Liansheng and Zhang, Xinke and Li, Chenhui and He, Dongjiao and Pan, Yihan and Yi, Jianjun},
  journal = {IEEE Robotics and Automation Letters},
  year    = {2026},
  volume  = {11},
  number  = {3},
  pages   = {2666--2673},
  doi     = {10.1109/LRA.2026.3653372}
}

Update Logs

Click to expand Update Logs (click to collapse)

• 2026-01-04

  • Separate ROS interface and algorithm.
  • Refactor SuperLIOReLoc to inherit from SuperLIO.
  • Code style aligned with ROS2 version.

• 2026-01-04

  • The main branch is renamed to ros1
  • add ros2 branch

• 2026-01-04 21:51

  • release ROS2 version