论文标题
使用动态逆运动学和联络辅助谎言组Kalman滤波器的全身人运动学估算
Whole-Body Human Kinematics Estimation using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter
论文作者
论文摘要
在没有位置传感器的情况下，通过可穿戴感应技术对人类的全身运动估计是具有挑战性的。本文有助于使用可穿戴分布的惯性和强度传感的基于模型的全身运动学估计算法的开发。这是通过扩展现有的基于动态优化的反向运动学（IK）方法来进行联合状态估计的方法来完成的，该方法在级联中包括一个基于压力的接触探测器中心和与浮动基础姿势估计的Lie组的基于压力的触点检测器和一个联络辅助的Kalman过滤器。在实验场景中测试了所提出的方法，其中配备有传感的西装和鞋子的人进行步行运动。提出的方法被证明是为了获得全身人类运动的可靠重建。
Full-body motion estimation of a human through wearable sensing technologies is challenging in the absence of position sensors. This paper contributes to the development of a model-based whole-body kinematics estimation algorithm using wearable distributed inertial and force-torque sensing. This is done by extending the existing dynamical optimization-based Inverse Kinematics (IK) approach for joint state estimation, in cascade, to include a center of pressure-based contact detector and a contact-aided Kalman filter on Lie groups for floating base pose estimation. The proposed method is tested in an experimental scenario where a human equipped with a sensorized suit and shoes performs walking motions. The proposed method is demonstrated to obtain a reliable reconstruction of the whole-body human motion.
