不変カルマンフィルタとスケール可変ロバストコスト関数を用いた四脚ロボットの固有感覚状態推定
Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions
固有感覚センサのみを用いた四脚ロボットの状態推定のため、不変拡張カルマンフィルタとロバストコスト関数を組み合わせた手法を提案し、公開データセットで従来手法より最大40%の姿勢ドリフト低減を達成した。
著者: Hilton Marques Souza Santana, João Carlos Virgolino Soares, Ylenia Nisticò, Marco Antonio Meggiolaro, Claudio Semini
分類: cs.RO
原文アブストラクト
Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especially in scenarios with uneven and slippery terrain. This paper presents a new Invariant Extended Kalman filter for legged robot state estimation using only proprioceptive sensors. We formulate the methodology by combining recent advances in state estimation theory with the use of robust cost functions in the measurement update. We tested our methodology on quadruped robots through experiments and public datasets, showing that we can obtain a pose drift up to 40% lower in trajectories covering a distance of over 450m, in comparison with a state-of-the-art Invariant Extended Kalman filter.
