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.

翻译：状态估计对腿式机器人至关重要，它直接影响控制性能与运动稳定性。本文提出一种自适应不变扩展卡尔曼滤波方法，旨在提升腿式机器人的本体感知状态估计精度。该方法基于在线协方差估计自适应调整接触足模型的噪声水平，从而在变化接触条件下实现更优的状态估计。传统滑移抑制方法无法有效处理微小滑移，且过于敏感的滑移抑制设置可能导致滤波器发散，本方法对此类问题具有良好适应性。我们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。通过在四足机器人LeoQuad上进行实际场景实验验证，该方法在动态运动场景中展现出显著提升的状态估计性能。