论文标题
通过使用扩展的卡尔曼过滤器融合IMU和RGB摄像机数据，人腿运动跟踪
Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter
论文作者
论文摘要
人类运动捕获经常用于研究康复和临床问题，并为娱乐业提供逼真的动画。基于IMU的系统以及基于标记的运动跟踪系统，由于其实施成本低和轻量级而是跟踪移动的最流行方法。本文提出了一种基于QUATERNION的扩展Kalman滤波器方法，该方法使用一组与摄像头标记系统数据融合的IMU传感器数据恢复了人腿部细分运动。在本文中，开发了一种扩展的Kalman滤波器方法，以融合两个IMU的数据和一个用于人腿运动跟踪的RGB摄像机。基于惯性传感器和摄像头系统的互补属性，在引入的新测量模型中，大腿和下层的方向数据通过三个测量方程进行更新。通过相机标记系统的骨盆关节的跟踪位置使人体的定位成为可能。数学模型已被用来估计2D图像中关节的深度。通过光学运动跟踪器系统评估所提出的算法的效率。
Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry. IMU-based systems, as well as Marker-based motion tracking systems, are the most popular methods to track movement due to their low cost of implementation and lightweight. This paper proposes a quaternion-based Extended Kalman filter approach to recover the human leg segments motions with a set of IMU sensors data fused with camera-marker system data. In this paper, an Extended Kalman Filter approach is developed to fuse the data of two IMUs and one RGB camera for human leg motion tracking. Based on the complementary properties of the inertial sensors and camera-marker system, in the introduced new measurement model, the orientation data of the upper leg and the lower leg is updated through three measurement equations. The positioning of the human body is made possible by the tracked position of the pelvis joint by the camera marker system. A mathematical model has been utilized to estimate joints' depth in 2D images. The efficiency of the proposed algorithm is evaluated by an optical motion tracker system.
