This paper describes a technique for using magnetic motion capture data to determine the joint parameters of an articulated hierarchy. This technique makes it possible to determine limb lengths, joint locations, and sensor placement for a human subject without external measurements. Instead, the joint parameters are inferred with high accuracy from the motion data acquired during the capture session. The parameters are computed by performing a linear least squares fit of a rotary joint model to the input data. A hierarchical structure for the articulated model can also be determined in situations where the topology of the model is not known. Once the system topology and joint parameters have been recovered, the resulting model can be used to perform forward and inverse kinematic procedures. We present the results of using the algorithm on human motion capture data, as well as validation results obtained with data from a simulation and a wooden linkage of known dimensions.

翻译：本文描述了一种利用磁性运动捕捉数据确定铰接层次结构中关节参数的技术。该技术无需外部测量即可确定人体肢体的长度、关节位置及传感器放置方式，而是通过捕捉过程中获取的运动数据高精度地推断关节参数。通过将旋转关节模型对输入数据进行线性最小二乘拟合，即可计算出这些参数。在模型拓扑结构未知的情况下，还可确定铰接模型的层次结构。一旦恢复系统拓扑结构与关节参数，得到的模型即可用于执行正向与逆向运动学计算。我们展示了该算法在人体运动捕捉数据上的应用结果，并通过仿真数据及已知尺寸的木质连杆模型获得的验证结果。