arXiv 2503.050772025-03-07cs.RO

Adaptive-LIO：通过环境自适应增强激光雷达惯性里程计的鲁棒性与精度

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

State Key Laboratory of Robotics and Systems（机器人学与系统国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Hangzhou Qisheng Intelligent Techology Co. Ltd.（杭州齐盛智能科技有限公司）；School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）；School of Mechanical and Electrical Engineering, University of Electronic Science And Technology（电子科技大学机械与电气工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）

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

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AI总结：

本文提出自适应激光雷达惯性里程计Adaptive-LIO，通过自适应分割、IMU饱和故障检测与多分辨率体素地图调整，解决帧间隔、IMU饱和及室内外切换导致的精度下降问题，提升定位鲁棒性与精度。

摘要：

新兴的物联网应用（如无人驾驶汽车）对高精度定位与导航的需求日益增长。当前，激光雷达惯性里程计在机器人与自动驾驶领域日益普及。然而，许多现有SLAM系统对不同场景的适应性不足。面临的挑战包括：在恒定速度假设下，帧间隔变长会导致点云精度下降；当IMU饱和发生时，错误的IMU信息会被耦合进系统；在室内外场景切换时，使用固定分辨率地图会导致定位精度降低。为解决这些问题，我们提出了一种松耦合的自适应激光雷达惯性里程计，名为Adaptive-LIO。该方法引入自适应分割以提升建图精度，通过IMU饱和与故障检测自适应调整运动模态，并基于距激光雷达中心的距离利用多分辨率体素地图自适应调整地图分辨率。我们提出的方法已在多种具有挑战性的场景中进行了测试，验证了所引入改进的有效性。代码已在GitHub上开源：Adaptive-LIO。

英文摘要：

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 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 \textbf{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 code is open-source on GitHub: \href{https://github.com/chengwei0427/adaptive_lio}{Adaptive-LIO}.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。