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

作     者：Zhao, Chengwei Hu, Kun Xu, Jie Zhao, Lijun Han, Baiwen Wu, Kaidi Tian, Maoshan Yuan, Shenghai 

作者机构：Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China Hangzhou Qisheng Intelligent Techol Co Ltd Hangzhou 311217 Zhejiang Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：12123-12136页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Tasks of the State Key Laboratory of Robotics and Systems (HIT) [SKLRS-2025-KF-16, SKLRS202417B] 

主　　题：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 

摘      要：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 (LIO) 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 LIO 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 multiresolution 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 code is open-source on GitHub: Adaptive-LIO.