Unmanned aerial vehicle position heading regression method and device for cross-view scene

By constructing a UAV position and heading regression method across perspective scenarios, and utilizing cross-attention mechanism and VGG backbone network, the positioning and navigation problem of UAVs when GNSS signals are lost in extreme environments is solved. This achieves high-precision absolute position and heading angle prediction, and reduces sensor dependence and migration bias from simulation to real scene.

CN121297867AActive Publication Date: 2026-01-09ZHEJIANG UNIV
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Patent Information

Application Number
CN202511861617.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-11
Publication Date
2026-01-09
Estimated Expiration
2045-12-11

AI Technical Summary

Technical Problem

When drones cannot receive GNSS signals in extreme environments, existing visual positioning and navigation algorithms suffer from problems such as short navigation distance, image-text alignment deviation, large model size making deployment difficult, and susceptibility to communication interference. Furthermore, the positioning accuracy of remote sensing maps is not high, making it difficult to achieve continuous point-to-point navigation.

Method used

A method for UAV position and heading regression across different viewpoints is constructed. A CVPHR model is established using a gridded continuous 2D remote sensing map and 3D UAV view samples. Visual features are extracted using a cross-attention mechanism. Combined with a VGG backbone network and a CSMG feature extractor, the absolute position and heading angle of the UAV are predicted.

Benefits of technology

It improves the positioning accuracy and navigation capabilities of UAVs in extreme environments, reduces reliance on sensors, and the lightweight model is suitable for scenarios with limited computing power, thus reducing performance deviations when migrating from simulation to real-world scenarios.

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Abstract

The invention discloses an unmanned aerial vehicle autonomous positioning navigation method and device based on a cross-view azimuth regression network. The method comprises the steps of 1, constructing a cross-view-angle data set which is oriented to a cross-view-angle scene and contains azimuth information, 2, constructing an unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene, and 3, training and verifying the unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene by using a training set and a verification set. And 4, carrying out regression performance test on the unmanned aerial vehicle position heading regression model facing the cross-view scene by using the test set. The method only depends on a visual image, can reduce the dependence degree of unmanned aerial vehicle positioning navigation on a sensor, improves the survivability in an extreme environment, uses a cross-visual-angle remote sensing map to construct simulation training data and generate the absolute orientation regression capability, can eliminate the drift problem, and also has natural advantages in the aspect of migration from simulation to a real scene. The model is light in weight and is more suitable for unmanned aerial vehicle continuous positioning and directional navigation scenes with limited computing power resources.
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