RFG-TVIU:对于紧密结合的视觉/IMU/UWB集成,一个强大的因子图
Gongjun Fan1, Qing Wang2, Gaochao Yang3
1CCCC Investment Company Limited, Beijing, China.
Frontiers in neurorobotics
|May 14, 2024
概括
这项研究引入了一种新的Vision/IMU/UWB传感器融合系统,用于精确的导航. 适应因子图模型提高了准确性,特别是在具有挑战性的非视线环境中.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 传感器融合式传感器
- 导航系统 导航系统
背景情况:
- 在各种应用中,高精度导航至关重要.
- 单个传感器对于不同的导航场景是不够的.
- 现有的多传感器系统面临着传感器可用性和数据权重方面的挑战.
研究的目的:
- 开发一个"插即用"的Vision/IMU/UWB多传感器紧紧合系统.
- 为了实现UWB基站坐标的实时估计,无需预校准.
- 提出一个适应性强大的因子图模型,解决动态传感器可用性和观测权重.
主要方法:
- 一个基于因子图的紧密合系统,集成Vision,IMU和超宽带 (UWB) 传感器.
- 实时估计UWB基站坐标作为系统参数.
- 一个适应性强大的因子图模型,用于动态传感器可用性和加权的观测信息.
- 一种新的适应性估计模型,用于UWB范围的协差,没有先前的系统信息.
主要成果:
- 与最先进的方法相比,拟议的系统表现出优越的性能.
- 实现了显著的根平均平方误差 (RMSE) 改进:62.83-64.26%在第1场景和70.32-82.15%在第2场景 (非视线).
- 适应性UWB协差估计有效处理实时变化.
结论:
- 视觉/IMU/UWB紧密合的系统提供了强大的,高精度的导航.
- 适应因子图的方法在动态传感器环境中提高了可靠性.
- 该系统提供了显著的性能提升,特别是在具有挑战性的非视线条件下.
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