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Article

UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes

1
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
2
School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450001, China
3
Dengzhou Water Conservancy Bureau, Dengzhou 474150, China
*
Author to whom correspondence should be addressed.
Academic Editor: Chris Rizos
Sensors 2022, 22(15), 5862; https://doi.org/10.3390/s22155862 (registering DOI)
Received: 17 June 2022 / Revised: 25 July 2022 / Accepted: 2 August 2022 / Published: 5 August 2022
(This article belongs to the Section Navigation and Positioning)
With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations. View Full-Text
Keywords: UAV; multi-source fusion; factor graph optimization; robustness UAV; multi-source fusion; factor graph optimization; robustness
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MDPI and ACS Style

Dai, J.; Liu, S.; Hao, X.; Ren, Z.; Yang, X. UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes. Sensors 2022, 22, 5862. https://doi.org/10.3390/s22155862

AMA Style

Dai J, Liu S, Hao X, Ren Z, Yang X. UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes. Sensors. 2022; 22(15):5862. https://doi.org/10.3390/s22155862

Chicago/Turabian Style

Dai, Jun, Songlin Liu, Xiangyang Hao, Zongbin Ren, and Xiao Yang. 2022. "UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes" Sensors 22, no. 15: 5862. https://doi.org/10.3390/s22155862

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