脚式ロボットの状態推定のための適応不変拡張カルマンフィルタ
Adaptive Invariant Extended Kalman Filter for Legged Robot State Estimation
接触足モデルのノイズをオンライン共分散推定に基づいて適応的に調整し、接触状態の変化や小さな滑りに対応する脚式ロボットの固有受容状態推定手法を提案。四足ロボットLeoQuadでの実機実験で動的歩行時の推定性能向上を実証した。
著者: Kyung-Hwan Kim, DongHyun Ahn, Dong-hyun Lee, JuYoung Yoon, Dong Jin Hyun
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.