Vision-based guidance for fixed-wing unmanned aerial vehicle autonomous carrier landing

Autonomous carrier landing is regarded as a crucial problem among the flight stages of carrier-based unmanned aerial vehicle. In recent years, vision-based guidance has become a promising solution for unmanned aerial vehicle autonomous carrier landing. In this paper a new vision-based navigation scheme is proposed for unmanned aerial vehicle autonomous carrier landing. The scheme aims at dealing with two core problems: searching the carrier by using the images obtained from the airborne forward-looking camera and estimating the relative position and attitude between the unmanned aerial vehicle and the carrier. In order to solve the first problem, the spectral residual-based saliency analysis method is firstly adopted to obtain the Region of Interest. Then the locality-constraint linear coding-based feature learning method is proposed for feature extraction, and the region of interest containing the carrier is finally recognized by the linear support vector machine. In order to solve the second problem, five feature points are firstly selected on the surface of the carrier. Then, a new carrier-fixed moving reference coordinate system is set up. The six landing parameters including three attitude parameters and three position parameters are finally obtained by using orthogonal iteration. The experiment results verify the superiority and effectiveness of the algorithms proposed in this paper.