视觉 - 惯性 - 车轮计量与滑动补偿和动态特征消除
Niraj Reginald1, Omar Al-Buraiki1, Thanacha Choopojcharoen1
1Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究引入了一种新的方法来改善机器人导航,通过使用先进的机器学习技术来补偿视觉惯性轮距测量 (VIWO) 中的轮子滑动,以实现更准确的定位.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 传感器融合式传感器
背景情况:
- 机器人定位和测距因传感器不确定性和轮子滑动而面临挑战.
- 视觉惯性轮距测量 (VIWO) 结合了多个传感器输入以实现强大的导航.
- 现有的VIWO系统在具有挑战性的地形和动态环境中难以提供准确的性能.
研究的目的:
- 开发一种新的数据驱动方法来补偿VIWO系统中的轮子滑动.
- 为了提高机器人定位和测距的准确性和稳定性.
- 通过解决动态特征点来改善视觉和惯性测量的集成.
主要方法:
- 使用高斯过程回归 (GPR) 与深度内核学习来建模和减轻滑动诱导的错误.
- 嵌入了长短期内存 (LSTM) 层,用于高级错误建模.
- 开发了一个特征信任估计器来处理视觉数据中的动态特征点.
- 采用多状态约束卡尔曼波器 (MSCKF) 来进行状态估计.
主要成果:
- 拟议的方法有效地弥补了轮子的滑动,大大提高了定位的准确性.
- 该系统在具有挑战性的地形和动态环境中表现出增强的稳定性.
- 与传统的VIWO系统相比,GPR和LSTM层的集成带来了更高的性能.
- 实验验证证了使用真实世界数据集的方法的有效性.
结论:
- 这种新的数据驱动方法有效地解决了VIWO中的轮子滑动和动态特征点问题.
- 增强的VIWO系统为自主导航提供了更高的准确性和稳定性.
- 这项研究有助于推进用于不可预测环境的多传感器融合和导航技术.
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