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<p align="center"> <h1 align="center">LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters</h1> <p align="center"> <a href="https://arxiv.org/pdf/2311.09887"><img src="https://img.shields.io/badge/Paper-pdf-<COLOR>.svg?style=flat-square" /></a> <a href="https://github.com/YibinWu/LIO-EKF"><img src="https://img.shields.io/ros/v/noetic/moveit_msgs.svg" /></a> <a href="https://github.com/YibinWu/LIO-EKF/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square" /></a> </p> </p>

TL;DR: LIO-EKF is a lightweight yet efficient LiDAR-inertial odometry system based on adaptive point-to-point registration and EKF.

1. Prerequisites

2. RUN LIO-EKF

2.1 Clone the repository and catkin_make

cd ~/catkin_ws/src
git clone git@github.com:YibinWu/LIO-EKF.git
cd ../
catkin_make
source ~/catkin_ws/devel/setup.bash

2.2 Download the datasets

2.3 Run it

Replace the path to the rosbag (bagfile) in the launch files with your own path.

roslaunch lio_ekf urbanNav20210517.launch 
roslaunch lio_ekf street_01.launch
roslaunch lio_ekf short_exp.launch

3. Citation

If you find our study helpful to your academic work, please consider citing the paper.

@inproceedings{wu2024icra,
    author     = {Wu, Yibin and Guadagnino, Tiziano and Wiesmann, Louis and Klingbeil, Lasse and Stachniss, Cyrill and Kuhlmann, Heiner},
    title      = {{LIO-EKF}: High Frequency {LiDAR}-Inertial Odometry using Extended {Kalman} Filters},
    booktitle  = {IEEE International Conference on Robotics and Automation (ICRA)},
    year       = {2024}
}

4. Contact

If you have any questions, please feel free to contact Mr. Yibin Wu {yibin.wu@igg.uni-bonn.de}.

5. Acknowledgement

Thanks a lot to KISS-ICP, which has inspired this work, and KF-GINS.