非慣性環境における脚式ロボットの状態推定
Legged Robot State Estimation within Non-inertial Environments
回転・並進するトレッドミル上を歩くヒューマノイドロボットの状態を、不変拡張カルマンフィルタ（InEKF）を用いて推定する手法を提案し、実機実験で高い収束性と精度を示した。
著者: Zijian He, Sangli Teng, Tzu-Yuan Lin, Maani Ghaffari, Yan Gu
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper investigates the robot state estimation problem within a non-inertial environment. The proposed state estimation approach relaxes the common assumption of static ground in the system modeling. The process and measurement models explicitly treat the movement of the non-inertial environments without requiring knowledge of its motion in the inertial frame or relying on GPS or sensing environmental landmarks. Further, the proposed state estimator is formulated as an invariant extended Kalman filter (InEKF) with the deterministic part of its process model obeying the group-affine property, leading to log-linear error dynamics. The observability analysis of the filter confirms that the robot's pose (i.e., position and orientation) and velocity relative to the non-inertial environment are observable. Hardware experiments on a humanoid robot moving on a rotating and translating treadmill demonstrate the high convergence rate and accuracy of the proposed InEKF even under significant treadmill pitch sway, as well as large estimation errors.
