ROCIP:强大的连续惯性位置跟踪,用于从人类行为者和环境的相互作用中产生的复杂行为
1Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ UK.
这项研究介绍了ROCIP,这是一种用于惯性导航的新型系统,可以减少错误积累. ROCIP使用神经统计运动模型和Rao-Blackwellised粒子过器来准确,长期的行人死亡计算 (PDR).
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 消费级惯性测量单元 (IMU) 正在改善,但受到噪音和偏差的影响.
- 步行者死亡计算 (PDR) 方法在长期错误积累方面遇到了困难.
- 整合惯性跟踪与全球参考框架仍然是一个挑战.
研究的目的:
- 开发一个用于惯性导航的"错误内观"系统.
- 克服传统PDR和机器学习方法的局限性.
- 为了实现准确的,长期的独立惯性跟踪.
主要方法:
- 使用DenseNet预测姿势和不确定性的神经统计运动模型.
- 与Rao-Blackwellised颗粒过器 (RBPF) 集成,用于概率校准.
- 收集了一套具有多种运动模式的新型头部装载IMU数据集.
主要成果:
- 拟议的ROCIP方法在惯性跟踪方面表现优于领先的方法.
- 实现了4.94m的相对轨迹误差 (RTE) 和4.36m的绝对轨迹误差 (ATE).
- 在长期跟踪过程中证明了持续的准确性和最小的错误积累.
结论:
- ROCIP有效地解决了死计算中的错误积累问题.
- 该系统为长期,独立的惯性跟踪提供了强大的解决方案.
- 这种进步在需要精确运动跟踪的领域具有广泛的应用.
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