基于径向基函数逼近的腿轮机器人地形感知激光雷达-惯性里程计
Terrain-Awared LiDAR-Inertial Odometry for Legged-Wheel Robots Based on Radial Basis Function Approximation
AI总结：
针对腿轮机器人在非结构化地形中现有里程计易因忽略地形几何出现位姿漂移的问题，提出基于径向基函数逼近的地形感知LIO框架，结合GPU并行保障实时性，提升了复杂地形下的定位精度。
摘要：
精准的里程计对于在颠簸路面、楼梯等非结构化地形中作业的腿轮机器人至关重要。现有方法因忽略地形几何特性，常存在位姿漂移问题。我们提出一种地形感知的激光雷达-惯性里程计（LIO）框架，采用径向基函数（RBF）逼近地形，其中心被自适应选取，权重可递归更新。生成的平滑地形流形提供“软约束”，对里程计优化进行正则化，缓解机器人机动过程中高程突变下的z轴位姿漂移。为保障LIO的实时性能，我们进一步通过GPU并行化计算RBF相关项，并求解稀疏核矩阵的逆。在非结构化地形上的实验表明，与现有最优基线方法相比，本文方法的定位精度更高，在存在连续高度变化或高度突变时特征稀疏的场景中表现尤为突出。
英文摘要：
An accurate odometry is essential for legged-wheel robots operating in unstructured terrains such as bumpy roads and staircases. Existing methods often suffer from pose drift due to their ignorance of terrain geometry. We propose a terrain-awared LiDAR-Inertial odometry (LIO) framework that approximates the terrain using Radial Basis Functions (RBF) whose centers are adaptively selected and weights are recursively updated. The resulting smooth terrain manifold enables ``soft constraints" that regularize the odometry optimization and mitigates the $z$-axis pose drift under abrupt elevation changes during robot's maneuver. To ensure the LIO's real-time performance, we further evaluate the RBF-related terms and calculate the inverse of the sparse kernel matrix with GPU parallelization. Experiments on unstructured terrains demonstrate that our method achieves higher localization accuracy than the state-of-the-art baselines, especially in the scenarios that have continuous height changes or sparse features when abrupt height changes occur.
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。
