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}.

翻译：随着无人驾驶汽车等新兴物联网（IoT）应用的发展，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学与自动驾驶领域应用日趋广泛。然而，现有许多SLAM系统对不同场景的适应性不足，主要面临以下挑战：在恒定速度假设下，随着帧间隔增大点云精度下降；当IMU发生饱和时，错误IMU信息与系统状态产生耦合；在室内-室外场景转换过程中，固定分辨率地图的使用导致定位精度降低。为解决这些问题，本文提出一种松耦合自适应激光雷达-惯性里程计系统（\textbf{Adaptive-LIO}），该系统通过自适应分割提升建图精度，基于IMU饱和与故障检测实现运动模式自适应，并利用基于激光雷达中心距离的多分辨率体素地图动态调整地图分辨率。所提方法已在多种挑战性场景中进行测试，验证了改进措施的有效性。代码已在GitHub开源：\href{https://github.com/chengwei0427/adaptive_lio}{Adaptive-LIO}。