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- Title
RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration.
- Authors
Gongjun Fan; Qing Wang; Gaochao Yang; Pengfei Liu
- Abstract
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 tightlycoupled 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 realtime 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).
- Subjects
PARAMETER estimation; INFORMATION measurement; INFORMATION storage &; retrieval systems; GRAPH algorithms
- Publication
Frontiers in Neurorobotics, 2024, p01
- ISSN
1662-5218
- Publication type
Article
- DOI
10.3389/fnbot.2024.1343644