Binocular stereo vision-based relative positioning algorithm for drone swarm
Abstract To address the challenges of high computational complexity and poor real-time performance in binocular vision-based Unmanned Aerial Vehicle (UAV) formation flight, this paper introduces a UAV localization algorithm based on a lightweight object detection model. Firstly, we optimized the YOL...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-86981-1 |
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| Summary: | Abstract To address the challenges of high computational complexity and poor real-time performance in binocular vision-based Unmanned Aerial Vehicle (UAV) formation flight, this paper introduces a UAV localization algorithm based on a lightweight object detection model. Firstly, we optimized the YOLOv5s model using lightweight design principles, resulting in Yolo-SGN. This model achieves a 65.5% reduction in parameter count, a 62.7% reduction in FLOPs, and a 1.8% increase in accuracy compared to the original detection model. Subsequently, Yolo-SGN is utilized to extract target regions from binocular images, and feature point matching is exclusively conducted within these regions to minimize unnecessary computations in non-target areas. Experimental results demonstrate that the combination of Yolo-SGN and the Oriented FAST and Rotated BRIEF (ORB) algorithm reduces feature point matching computations to only a quarter of those in the original ORB algorithm, significantly enhancing real-time performance. Finally, the extracted feature points from UAVs are input into a binocular vision localization model to compute their three-dimensional coordinates. The average of the three-dimensional coordinates of all feature points is used to determine the three-dimensional position of the target UAV. Experimental results confirm that the UAV binocular vision localization algorithm, based on a lightweight object detection model, exhibits exceptional performance in terms of precision and real-time capabilities. |
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| ISSN: | 2045-2322 |