Shi Yutong, Zhang Yongbo, Li Zhonghan, Yuan Shangwu, Zhu Shihao
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.
Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo 315100, China.
Sensors (Basel). 2023 Jul 26;23(15):6700. doi: 10.3390/s23156700.
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.
运动捕捉系统极大地推动了航空航天领域人机交互的研究。鉴于光学运动捕捉系统成本高昂且易受光照条件影响，同时考虑到惯性测量单元（IMU）传感器存在的漂移问题，本文采用一种融合方法，利用低成本可穿戴传感器对上肢混合运动进行跟踪。我们提出了一种新颖的算法，该算法将四阶龙格 - 库塔（RK4）马德威克互补方向滤波器与卡尔曼滤波器相结合，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。马德威克RK4方向滤波器用于通过磁、角速率和重力（MARG）系统的最优融合来补偿陀螺仪漂移，实施时无需了解噪声分布。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。采用四个锚点的立方分布，将UWB定位卡尔曼滤波器获得的无漂移位置用于融合IMU计算出的位置。所提出的算法已通过各种运动进行了测试，从IMU方法到IMU/UWB融合方法，均方根误差（RMSE）平均降低了1.2厘米。实验结果表明，我们所提出的算法在精确跟踪人体上肢运动方面具有很高的可行性和稳定性。
