The increasing amount of space debris in recent years has greatly threatened space operation. In order to ensure the safety level of spacecraft, space debris perception via on-orbit visual sensors has become a promising solution. However, the perception capability of visual sensors largely depends on illumination, which tends to be insufficient in dark environments. Since the images captured by visible and infrared sensors are highly complementary in dark environments, a convolutional sparse representation-based visible and infrared image fusion algorithm is proposed in this paper to expand the applicability of visual sensors. In particular, the local contrast measure is applied to obtain the refined weight map for fusing the base layers, which is more robust in a dark space environment. The algorithm can settle two significant problems in space debris surveillance, namely, improving the signal-noise ratio in a noise space environment and preserving more detailed information in a dark space environment. A space debris dataset containing registered visible and infrared images has been purposely created and used for algorithm evaluation. Experimental results demonstrate that the proposed method in this paper is effective for enhancing image qualities and can achieve favorable effects compared to other state-of-the-art algorithms.
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.
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