联合追踪器:实时惯性动力链追踪与联合位置估计
Bertram Taetz1,2, Michael Lorenz1, Markus Miezal1
1Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany.
Open research Europe
|July 2, 2024
概括
这项研究引入了无校准的惯性运动捕获,可以在不需要预先设置的情况下准确地在线估计传感器姿势和人类骨关节位置. 这为各种应用领域的现场运动分析带来了进步.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 传感器技术 传感器技术
背景情况:
- 现场人类运动捕捉 (HMC) 正在获得吸引力,基于摄像头的无标记器系统面临着视野受限和遮等局限性.
- 惯性运动捕捉为实验室外的运动跟踪提供了一个无阻塞的替代方案,但需要空间注册 (传感器校准).
研究的目的:
- 开发一种不需要校准的方法来进行惯性运动捕获.
- 为了能够同时在线估计传感器姿势和关节位置的动力链,如人类骨.
主要方法:
- 提出了一种递归估计器,用于同时在线估计传感器姿势和关节位置.
- 提供了从优化目标的推导.
- 适用于来自机身上的惯性传感器网络的同步数据流.
主要成果:
- 证明成功评估来自三环链的噪音模拟数据.
- 在25名健康个体的真实下半身行走数据上进行了验证.
- 用人形机器人的行走数据进行了测试.
结论:
- 拟议的方法可以实现无校准的惯性运动捕获.
- 方便在线精确估计人类骨动力学.
- 为现场运动分析和人机交互提供了强大的解决方案.
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