動的逆運動学と接触支援リー群カルマンフィルタによる全身人体キネマティクス推定
Whole-Body Human Kinematics Estimation using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter
ウェアラブル慣性・力覚センサのみで、動的逆運動学と接地検出を組み合わせたリー群カルマンフィルタにより人体の全身運動を推定する手法を提案し、歩行動作で有効性を示した。
著者: Prashanth Ramadoss, Lorenzo Rapetti, Yeshasvi Tirupachuri, Riccardo Grieco, Gianluca Milani, Enrico Valli, Stefano Dafarra, Silvio Traversaro, Daniele Pucci
分類: cs.RO
原文アブストラクト
Full-body motion estimation of a human through wearable sensing technologies is challenging in the absence of position sensors. This paper contributes to the development of a model-based whole-body kinematics estimation algorithm using wearable distributed inertial and force-torque sensing. This is done by extending the existing dynamical optimization-based Inverse Kinematics (IK) approach for joint state estimation, in cascade, to include a center of pressure-based contact detector and a contact-aided Kalman filter on Lie groups for floating base pose estimation. The proposed method is tested in an experimental scenario where a human equipped with a sensorized suit and shoes performs walking motions. The proposed method is demonstrated to obtain a reliable reconstruction of the whole-body human motion.
