用径向基函数建模地形，减少机器人在复杂路面的定位漂移。
Terrain-Awared LiDAR-Inertial Odometry for Legged-Wheel Robots Based on Radial Basis Function Approximation
- 通过自适应选取中心点的径向基函数逼近地形表面
- 在有连续高程变化或特征稀疏时定位误差降低30%以上
- 适合需在崎岖路面长时间稳定运行的轮足机器人
在不规则地形（如颠簸路面、楼梯）中，精确的位姿估算是轮足机器人运行的关键。现有方法因忽略地形几何信息，常出现位姿漂移。本文提出一种基于径向基函数（RBF）近似的地形感知激光雷达-惯性里程计（LIO）框架：自适应选择RBF中心，递归更新权重，构建平滑地形流形，为位姿优化提供“软约束”，有效缓解突变高程下的$z$轴漂移。为保证实时性，采用GPU并行化计算RBF相关项及稀疏核矩阵求逆。在非结构化地形上的实验表明，本方法在连续高程变化或特征稀疏场景下，定位精度显著优于当前最优基线，尤其在剧烈起伏时表现更优。
原文摘要 · Abstract (English)
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.
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