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Tightly Coupled Multifactor Optimization-Based Hybrid Visual Localization for UAV
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AI
DOI：10.1109/TIM.2025.3597617.png)
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Abstract
En 中文
The localization method based on aerial images and satellite maps is an effective solution for unmanned aerial vehicles (UAVs) in Global Navigation Satellite Systems (GNSS) denied conditions. However, in addition to differences in scale and perspective, the difficulty of matching increases due to changes in lighting and terrain. At the same time, the search area of the satellite map relies on previous matching results, so previous errors can easily cause cascading effects, significantly reducing the accuracy of localization. To overcome these challenges, a multifactor tightly coupled visual localization method is proposed in this article, which avoids the interference of single-modal outliers by introducing multifactor joint optimization. Second, a search strategy on the satellite map is proposed, which continuously updates the candidate area based on the status of the UAV to improve the efficiency of the search and the accuracy of matching. Finally, an updated method of satellite map is designed to avoid matching failures caused by lagging satellite map updates. Multiple times of experiments are conducted using two different types of UAVs in different conditions, ranges, altitudes, and speeds to verify the effectiveness of the proposed method. Comparison with the state-of-the-art visual localization method shows that the proposed method improves the accuracy of localization by 27.1% and the efficiency of processing by 30.6%.
Keywords:
Image matching
sensor fusion
unmanned aerial vehicle (UAV)
visual localization
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W