Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry

Chengwei Zhao^#,1,2    Kun hu^#,3    Jie Xu^*,1,5    Lijun Zhao^*,1    Baiwen Han¹    Kaidi Wu³ Maoshan Tian⁴ Shenghai Yuan<sup>5

1</sup>HIT    ²Qisheng Intelligent Techology    ³CUMT(XuZhou)    ⁴UESTC    ⁵ NTU

#-co-first authors *-corresponding authors

About

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, LiDAR inertial odometry becomes increasingly prevalent in robotics and autonomous driving. However, many current SLAM systems lack sufficient adaptability to various scenarios. Challenges include decreased point cloud accuracy with longer frame intervals under the constant velocity assumption, coupling of erroneous IMU information when IMU saturation occurs, and decreased localization accuracy due to the use of fixed-resolution maps during indoor-outdoor scene transitions. To address these issues, we propose a loosely coupled adaptive LiDAR-Inertial-Odometry named Adaptive-LIO, which incorporates adaptive segmentation to enhance mapping accuracy, adapts motion modality through IMU saturation and fault detection, and adjusts map resolution adaptively using multi-resolution voxel maps based on the distance from the LiDAR center. Our proposed method has been tested in various challenging scenarios, demonstrating the effectiveness of the improvements we introduce.

📝 Updates

• [2024.12] - Adaptive-lio is accepted to JIOT 2024. 🚀
• [2025.03] - Source code released 🎉

Dataset

  

Dataset	Full Name	Duration (s)	Distance (km)	LiDAR Type
QiSheng	industrial	485	00	Velodyne VLP-16
QiSheng	industrial2	414	00	Velodyne VLP-16
QiSheng	park1	479	00	Velodyne VLP-16
QiSheng	park2	315	0.0	Velodyne VLP-16

End-to-end errors

Dataset	DLIO	LIO-SAM	Point-lio	Fast-lio2	IG-lio	Ours
industrial1	4.485	13.935	x	11.778	21.815	2.4824
industrial2	0.185	2.467	1.778	9.547	1.737	0.107
parking1	1.81	2.27	3.164	5.53	1.77	0.492

Quickly Run

Dependencies

• ceres 2.10
• opencv
• Eigen3
• yaml-cpp

Usage

1. Prerequisites Ubuntu and ROS

   Ubuntu >= 18.04. And Ubuntu 20.04 is recommended.

2. glog

    sudo apt-get install -y libgoogle-glog-dev

3. build

    cd ~/catkin_ws/src
    git clone https://github.com/chengwei0427/Adaptive-LIO.git
    cd ..
    catkin_make

4. Run

    source devel/setup.bash
    roslaunch adaptive_lio run.launch

Publications

We kindly recommend to cite our paper if you find this library useful:

@ARTICLE{10806842,
  author={Zhao, Chengwei and Hu, Kun and Xu, Jie and Zhao, Lijun and Han, Baiwen and Wu, Kaidi and Tian, Maoshan and Yuan, Shenghai},
  journal={IEEE Internet of Things Journal}, 
  title={Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Accuracy;Laser radar;Odometry;Motion segmentation;Simultaneous localization and mapping;Internet of Things;Robots;Feature extraction;Trajectory;Robustness;LiDAR Inertial Odometry;Adaptive;SLAM;Multi-Resolution Map},
  doi={10.1109/JIOT.2024.3519533}}

Acknowledgments

Thanks for CT-ICP, SR-LIO and slam_in_autonomous_driving.

Contributors