Invariant Extended Kalman Filtering for Human Motion Estimation with Imperfect Sensor Placement
Invariant Extended Kalman Filtering for Human Motion Estimation with Imperfect Sensor Placement
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DOI：
10.23919/acc53348.2022.9867745
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Zenan Zhu;S. M. R. Sorkhabadi;Yan Gu;Wenlong Zhang
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作者：
Zenan Zhu;S. M. R. Sorkhabadi;Yan Gu;Wenlong Zhang
本文介绍了一种新的不变扩展卡尔曼滤波器设计，即使存在传感器未对准和初始状态估计误差，也能产生实时状态估计和快速误差收敛，以估计人体运动。该滤波器融合了连接到身体（例如骨盆或胸部）的惯性测量单元（IMU）返回的数据和零站立脚速度的虚拟测量（即腿部里程计）。所提出的滤波器的关键新颖性在于其过程模型满足群仿射特性，同时滤波器通过将其随机过程模型表示为布朗运动并将误差纳入腿部里程计中来明确解决 IMU 放置误差。尽管测量模型不完善（即，它不具有不变的观测形式），因此其线性化依赖于状态估计，但实验结果表明，即使在 IMU 放置不准确和初始估计误差显着的情况下，所提出的滤波器在下蹲运动期间也能快速收敛（0.2 秒内）。
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.