自适应激光惯性里程计，提升复杂场景定位精度与鲁棒性
Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry
- 根据环境动态调整地图分辨率和运动模态
- 在室内外切换时定位误差降低37%
- 适合自动驾驶与机器人在多变环境中的导航
随着物联网应用（如自动驾驶）对高精度定位需求的增长，激光惯性里程计在机器人与自动驾驶领域日益普及。然而，现有SLAM系统在不同场景下适应性不足：恒定速度假设导致长帧间隔下点云精度下降，IMU饱和时信息耦合错误，以及室内外转换时使用固定分辨率地图造成定位误差。为此，本文提出松耦合自适应激光惯性里程计（Adaptive-LIO），通过自适应分割提升建图精度，基于IMU饱和与故障检测动态切换运动模态，并利用多分辨率体素地图根据距激光中心距离自适应调整分辨率。实验在多种挑战性场景中验证了方法的有效性，显著提升定位性能。代码已开源：https://github.com/chengwei0427/adaptive_lio。
原文摘要 · Abstract (English)
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}.
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