不完全なセンサ配置に対応する不変拡張カルマンフィルタによる人体運動推定
Invariant Extended Kalman Filtering for Human Motion Estimation with Imperfect Sensor Placement
IMUと脚オドメトリを融合し、センサの取り付け誤差や初期推定誤差があっても高速に収束する不変拡張カルマンフィルタを提案した。
著者: Zenan Zhu, Seyed Mostafa Rezayat Sorkhabadi, Yan Gu, Wenlong Zhang
分類: cs.RO
原文アブストラクト
This paper introduces a new invariant extended Kalman filter design that produces real-time state estimates and rapid error convergence for the estimation of the human body movement even in the presence of sensor misalignment and initial state estimation errors. The filter fuses the data returned by an inertial measurement unit (IMU) attached to the body (e.g., pelvis or chest) and a virtual measurement of zero stance-foot velocity (i.e., leg odometry). The key novelty of the proposed filter lies in that its process model meets the group affine property while the filter explicitly addresses the IMU placement error by formulating its stochastic process model as Brownian motions and incorporating the error in the leg odometry. Although the measurement model is imperfect (i.e., it does not possess an invariant observation form) and thus its linearization relies on the state estimate, experimental results demonstrate fast convergence of the proposed filter (within 0.2 seconds) during squatting motions even under significant IMU placement inaccuracy and initial estimation errors.
