基于虚拟场景距离测度的复合卡尔曼运动估计

Composite Kalman Motion Estimation Method Based on Virtual Distance

MEMS惯性传感器应用于人体动作捕捉,但由于传感器的系统误差,导致虚拟人不能精确、稳定的操作。为了实现对手臂运动的精确跟踪,通过对虚拟手臂运动规律的分析,基于虚拟场景中虚拟手臂的手指到物体中心之间的距离测度,结合虚拟手臂运动特点,设计了一种手臂运动的复合卡尔曼运动模型。该模型将手臂运动分为匀速模型、匀加速模型等几个典型阶段,根据距离测度的不同,用不同的模型去对虚拟手臂的运动做精确估计。通过实验验证,复合卡尔曼模型可以实现在虚拟场景中的虚拟手臂精确地运动估计。

MEMS inertial sensors applied to human motion capture, but because of the MEMS sensor system errors, the virtual people cannot operate precisely and stably. In order to achieve tracking the movement of an arm accurately, by analyzing the movement of a virtual arm, measuring the distance between the finger of the arm and the center of the object in virtual scene, combined with the virtual arm movement characteristics, a composite Kalman motion estimation method was designed. The model divided arm movement into a uniform model, a uniformly accelerated model and several typical phase models. Depending on the different distance measure, used the different models to make an accurate estimate of the movement of a virtual arm. Through experiments, the composite Kalman motion estimation method could estimate virtual arms in a virtual scene accurately.