# Adaptive-LIO
**Repository Path**: cxh110/Adaptive-LIO
## Basic Information
- **Project Name**: Adaptive-LIO
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-18
- **Last Updated**: 2025-07-18
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
The mapping details of Adaptive-LIO and other algorithms.
## Overview
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.
The system flow of Adaptive-LIO.
## :memo: Updates
- \[2024.12\] - **Adaptive-lio** is accepted to JIOT 2024. :rocket:
- \[2025.03\] - 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**
```bash
sudo apt-get install -y libgoogle-glog-dev
```
3. build
```bash
cd ~/catkin_ws/src
git clone https://github.com/chengwei0427/Adaptive-LIO.git
cd ..
catkin_make
```
4. Run
```bash
source devel/setup.bash
roslaunch adaptive_lio run.launch
```
## Publications
If your like our projects, please cite us and support us with a star 🌟.
We kindly recommend to cite [our paper](https://ieeexplore.ieee.org/document/10806842) if you find this library useful:
```latex
@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={2025},
volume={12},
number={9},
pages={12123-12136},
keywords={Accuracy;Laser radar;Odometry;Motion segmentation;Simultaneous localization and mapping;Internet of Things;Robots;Feature extraction;Trajectory;Robustness;Adaptive;LiDAR inertial odometry (LIO);multiresolution map;SLAM},
doi={10.1109/JIOT.2024.3519533}}
```
## Acknowledgments
Thanks for [CT-ICP](https://github.com/jedeschaud/ct_icp), [SR-LIO](https://github.com/ZikangYuan/sr_lio) and [slam_in_autonomous_driving](https://github.com/gaoxiang12/slam_in_autonomous_driving).
## Contributors
[](https://github.com/chengwei0427/Adaptive-LIO/stargazers)
[](https://github.com/chengwei0427/Adaptive-LIO/fork)
[](https://github.com/chengwei0427/Adaptive-LIO/issues)
