面向城市空中交通的无人机视觉定位技术

doi:10.7527/S1000-6893.2024.31168

Abstract

Abstract:

Drones in the Urban Air Mobility （UAM） environment are faced with the problems of unstable satellite navigation and positioning and signal interference， and existing additional navigation systems are faced with the challenges of requirement for high airborne computing power. This study proposes a lightweight UAV visual navigation and positioning system. By developing an image feature extraction framework that fuses state space modules， the prediction accuracy is significantly improved， and a triplet self-supervised training method based on Gaussian pyramid is implemented to enhance the robustness of the algorithm. By introducing a feature matching strategy based on sliding windows and similarity matrices， the feature matching process is optimized and the inference speed is significantly improved. Experiments on the Airsim simulation platform and real UAM flight scenarios show that this algorithm can provide accurate additional navigation and positioning data in complex environments. Multiple sets of ablation experiments and performance tests are conducted to verify the advanced nature and real-timeliness of the algorithm， as well as its capability to effectively reduce the computing power requirements. The results show that this system can not only improve the accuracy of UAV navigation， but also provide a feasible visual solution for positioning and navigation in urban air traffic environments.

Key words: low-altitude economy, UAV, visual positioning, state space module, self-supervised training, Airsim, feature matching

CLC Number:

V279

Ruokun QU, Zhiyuan WANG, Yelu LIU, Chenglong LI, Bo JIANG. UAV visual positioning technology for urban air mobility[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531168.

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References 29

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模型版本	模型大小/MB	推理速度/（帧·s^-1）
完整模型	329	0.82
轻量化模型	65	0.05

指标	早晨	正午	黄昏
均方根误差/m	5.11	4.79	6.86
最大误差/m	9.10	6.59	13.73
标准差/m	2.14	2.08	2.72

参数	数值
起飞质量/g	249
长，宽，高/m	298，373，101
最大可倾斜角度/（°）	35
机载内存/GB	2
飞行速度（无载荷）/（m·s^-1）	12
相机有效像素/10⁴	4 800
电池容量/mAh	2 590

指标	航路1（红色航路）			航路2（黄色航路）
指标	早晨	正午	黄昏	早晨	正午	黄昏
均方根误差/m	6.81	6.60	7.64	7.71	6.92	8.96
最大误差/m	10.97	7.38	14.47	11.14	7.74	16.79
标准差/m	3.13	2.29	3.61	3.91	3.07	3.98

时间段	光照强度/lx	图像信噪比/dB
早晨	4 521.49	28.78
正午	62 315.85	36.89
黄昏	548.05	19.74

UAV visual positioning technology for urban air mobility

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方法	均方根误差/m	最大误差/m	标准差/m
文献［5］	46.44	65.31	20.87
文献［13］	58.892	71.45	23.32
文献［17］	19.87
文献［19］	11.61
文献［22］	8.39	31.95	11.37
本文方法	6.60	7.38	2.29

滑动窗口	相似度矩阵	欧氏距离匹配	RMSE/m
		√	156.85（失败）
√		√	124.52（失败）
	√	√	17.42
√	√	√	6.86

指标	本文方法	注意力机制	卷积神经网络
平均每帧处理时间/s	0.05	0.21	2.98
每张图像单帧存储需求/kB	1.2	4.2	11

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