基于互补卡尔曼滤波的人体运动跟踪

Zhi-Bo Wang, Lin Yang, Zhipei Huang, Jiankang Wu, Zhiqiang Zhang, Lixin Sun
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引用次数: 3

摘要

小型化惯性测量单元(IMU)在许多运动捕捉应用中得到了广泛应用。为了克服IMU的稳定性和噪声问题,开发了合适的数据融合方法,从IMU数据中获得可靠的方向估计。提出了一种在人体运动捕捉系统中对姿态误差、陀螺仪偏置误差和磁扰动进行建模,并用互补卡尔曼滤波对状态变量误差进行补偿的方法。实验结果表明,该方法显著降低了累计方向估计误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human motion tracking based on complementary Kalman filter
Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.
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