Inertial motion capture systems widely use low-cost IMUs to obtain the orientation of human body segments, but these sensors alone are unable to estimate link positions. Therefore, this research used a SLAM method in conjunction with inertial data fusion to estimate link positions. SLAM is a method that tracks a target in a reconstructed map of the environment using a camera. This paper proposes quaternion-based extended and square-root unscented Kalman filters (EKF & SRUKF) algorithms for pose estimation. The Kalman filters use measurements based on SLAM position data, multi-link biomechanical constraints, and vertical referencing to correct errors. In addition to the sensor biases, the fusion algorithm is capable of estimating link geometries, allowing the imposing of biomechanical constraints without a priori knowledge of sensor positions. An optical tracking system is used as a reference of ground-truth to experimentally evaluate the performance of the proposed algorithm in various scenarios of human arm movements. The proposed algorithms achieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitude estimation. Compared to the EKF, the SRUKF algorithm presents a smoother and higher convergence rate but is 2.4 times more computationally demanding. After convergence, the SRUKF is up to 17% less and 36% more accurate than the EKF in position and attitude estimation, respectively. Using an absolute position measurement method instead of SLAM produced 80% and 40%, in the case of EKF, and 60% and 6%, in the case of SRUKF, less error in position and attitude estimation, respectively.

翻译：惯性运动捕捉系统广泛使用低成本IMU获取人体各节段的朝向信息，但仅凭这些传感器无法估算连杆位置。因此，本研究采用SLAM方法与惯性数据融合相结合来估算连杆位置。SLAM是一种利用相机在重构的环境地图中追踪目标的方法。本文提出基于四元数的扩展卡尔曼滤波算法和平方根无迹卡尔曼滤波算法（EKF与SRUKF）进行位姿估计。卡尔曼滤波器利用基于SLAM位置数据、多连杆生物力学约束和垂直参考系的测量值进行误差校正。除传感器偏差外，该融合算法还能估算连杆几何参数，从而在无需先验传感器位置信息的情况下施加生物力学约束。采用光学追踪系统作为真值基准，通过实验评估所提算法在人体手臂多种运动场景下的性能。所提算法在位置和姿态估计中分别达到5.87厘米和1.1度的精度。与EKF相比，SRUKF算法具有更平滑且更快的收敛速度，但计算需求是前者的2.4倍。收敛后，SRUKF在位置估计精度上比EKF低17%，在姿态估计精度上比EKF高36%。若采用绝对位置测量方法替代SLAM，EKF的位置和姿态估计误差分别减少80%和40%，SRUKF分别减少60%和6%。