Fan Gongjun, Wang Qing, Yang Gaochao, Liu Pengfei
CCCC Investment Company Limited, Beijing, China.
School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Front Neurorobot. 2024 Apr 29;18:1343644. doi: 10.3389/fnbot.2024.1343644. eCollection 2024.
High precision navigation and positioning technology, as a fundamental function, is gradually occupying an indispensable position in the various fields. However, a single sensor cannot meet the navigation requirements in different scenarios. This paper proposes a "plug and play" Vision/IMU/UWB multi-sensor tightly-coupled system based on factor graph. The difference from traditional UWB-based tightly-coupled models is that the Vision/IMU/UWB tightly-coupled model in this study uses UWB base station coordinates as parameters for real-time estimation without pre-calibrating UWB base stations. Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate real-time covariance changes of UWB ranging. The algorithm proposed in this study was extensively tested in real-world scenarios, and the results show that the proposed system is superior to the most advanced combination method in all cases. Compared with the visual-inertial odometer based on the factor graph (FG-VIO), the RMSE is improved by 62.83 and 64.26% in scene 1 and 82.15, 70.32, and 75.29% in scene 2 (non-line-of-sight environment).
高精度导航与定位技术作为一项基本功能，正在各个领域逐渐占据不可或缺的地位。然而，单一传感器无法满足不同场景下的导航需求。本文提出了一种基于因子图的“即插即用”视觉/惯性测量单元/超宽带（Vision/IMU/UWB）多传感器紧密耦合系统。与传统基于超宽带的紧密耦合模型不同的是，本研究中的视觉/惯性测量单元/超宽带紧密耦合模型使用超宽带基站坐标作为实时估计参数，无需对超宽带基站进行预校准。针对多传感器组合导航系统中传感器可用性的动态变化以及传统因子图在观测信息权重分配方面存在的严重问题，本研究提出了一种自适应鲁棒因子图模型。基于冗余测量信息，我们提出了一种新颖的超宽带测距协方差自适应估计模型，该模型不依赖系统的先验信息，能够自适应估计超宽带测距的实时协方差变化。本研究提出的算法在实际场景中进行了广泛测试，结果表明，所提出的系统在所有情况下均优于最先进的组合方法。与基于因子图的视觉惯性里程计（FG-VIO）相比，在场景1中均方根误差（RMSE）分别提高了62.83%和64.26%，在场景2（非视距环境）中分别提高了82.15%、70.32%和75.29%。
