自适应调整足端模型噪声，提升四足机器人状态估计精度
Adaptive Invariant Extended Kalman Filter for Legged Robot State Estimation
- 基于在线协方差估计动态调节足端接触模型噪声
- 有效处理传统方法忽略的小幅滑动，避免滤波发散
- 无需接触传感器，适合对硬件成本敏感的场景
状态估计对四足机器人的控制性能和运动稳定性至关重要。本文提出一种自适应不变扩展卡尔曼滤波器，用于改进四足机器人的本体感知状态估计。该方法基于在线协方差估计，自适应调整足端接触模型的噪声水平，从而在不同接触条件下实现更优的状态估计。相较于传统滑动拒绝策略，本方法能有效处理微小滑动问题，避免因过于敏感的设定导致滤波发散。同时，采用接触检测算法替代接触传感器，降低了对额外硬件的依赖。所提方法在四足机器人LeoQuad上通过真实世界实验验证，在动态运动场景中表现出更优的状态估计性能。
原文摘要 · Abstract (English)
State estimation is crucial for legged robots as it directly affects control performance and locomotion stability. In this paper, we propose an Adaptive Invariant Extended Kalman Filter to improve proprioceptive state estimation for legged robots. The proposed method adaptively adjusts the noise level of the contact foot model based on online covariance estimation, leading to improved state estimation under varying contact conditions. It effectively handles small slips that traditional slip rejection fails to address, as overly sensitive slip rejection settings risk causing filter divergence. Our approach employs a contact detection algorithm instead of contact sensors, reducing the reliance on additional hardware. The proposed method is validated through real-world experiments on the quadruped robot LeoQuad, demonstrating enhanced state estimation performance in dynamic locomotion scenarios.
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