PDF(Pubmed)
Abstract：
Motion capture systems have enormously benefited the research into human-computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion approach with low-cost wearable sensors for hybrid upper limb motion tracking. We propose a novel algorithm that combines the fourth-order Runge-Kutta (RK4) Madgwick complementary orientation filter and the Kalman filter for motion estimation through the data fusion of an inertial measurement unit (IMU) and an ultrawideband (UWB). The Madgwick RK4 orientation filter is used to compensate gyroscope drift through the optimal fusion of a magnetic, angular rate, and gravity (MARG) system, without requiring knowledge of noise distribution for implementation. Then, considering the error distribution provided by the UWB system, we employ a Kalman filter to estimate and fuse the UWB measurements to further reduce the drift error. Adopting the cube distribution of four anchors, the drift-free position obtained by the UWB localization Kalman filter is used to fuse the position calculated by IMU. The proposed algorithm has been tested by various movements and has demonstrated an average decrease in the RMSE of 1.2 cm from the IMU method to IMU/UWB fusion method. The experimental results represent the high feasibility and stability of our proposed algorithm for accurately tracking the movements of human upper limbs.
摘要：
运动捕获系统极大地受益于航空航天领域中对人机交互的研究。鉴于光学运动捕获系统的高成本和对照明条件的敏感性，以及考虑IMU传感器的漂移，本文利用一种低成本可穿戴传感器的融合方法进行混合上肢运动跟踪。我们提出了一种新颖的算法，该算法结合了四阶Runge-Kutta（RK4）Madgwick互补方向滤波器和Kalman滤波器，通过惯性测量单元（IMU）和超宽带（UWB）的数据融合来进行运动估计。MadgwickRK4定向滤波器用于通过磁的最佳融合来补偿陀螺仪漂移，角速度,和重力(MARG)系统，不需要噪声分布的知识来实施。然后，考虑到UWB系统提供的误差分布，我们采用卡尔曼滤波器来估计和融合UWB测量值，以进一步减少漂移误差。采用四个锚的立方体分布，UWB定位卡尔曼滤波器获得的无漂移位置用于融合IMU计算的位置。所提出的算法已通过各种运动进行了测试，并证明了从IMU方法到IMU/UWB融合方法的RMSE平均降低了1.2cm。实验结果表明，我们提出的算法可以准确跟踪人体上肢的运动，具有很高的可行性和稳定性。
