Deep learning enabled localization for UAV autolanding

This article concentrates on ground vision guided autonomous landing of a fixed-wing Unmanned Aerial Vehicle(UAV)within Global Navigation Satellite System(GNSS)denied environments.Cascaded deep learning models are developed and employed into image detection and its accuracy promoting for UAV autolanding,respectively.Firstly,we design a target bounding box detection network Bbox Locate-Net to extract its image coordinate of the flying object.Secondly,the detected coordinate is fused into spatial localization with an extended Kalman filter estimator.Thirdly,a point regression network Point Refine-Net is developed for promoting detection accuracy once the flying vehicle’s motion continuity is checked unacceptable.The proposed approach definitely accomplishes the closed-loop mutual inspection of spatial positioning and image detection,and automatically improves the inaccurate coordinates within a certain range.Experimental results demonstrate and verify that our method outperforms the previous works in terms of accuracy,robustness and real-time criterions.Specifically,the newly developed Bbox Locate-Net attaches over 500 fps,almost five times the published state-of-the-art in this field,with comparable localization accuracy.