RFG-TVIU:用于视觉/IMU/UWB 紧密耦合集成的稳健因子图

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-03-11 DOI:10.3389/fnbot.2024.1343644
Gongjun Fan, Qing Wang, Gaochao Yang, Pengfei Liu
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引用次数: 0

摘要

高精度导航定位技术作为一项基础功能,正逐渐在各个领域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 "Vision/IMU/UWB 多传感器紧耦合系统。与传统的基于 UWB 的紧耦合模型不同的是,本研究中的 Vision/IMU/UWB 紧耦合模型使用 UWB 基站坐标作为参数进行实时估计,而无需预先校准 UWB 基站。针对多传感器综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布方面的严重问题,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,我们提出了一种新型的 UWB 测距协方差自适应估计模型,该模型不依赖于系统的先验信息,可以自适应地估计 UWB 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都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,RMSE 分别提高了 62.83% 和 64.26%;在场景 2(非视距环境)中,RMSE 分别提高了 82.15%、70.32% 和 75.29%。
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RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration
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).
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
期刊最新文献
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