Chan-Vese model based binocular visual object extraction for UAV autonomous take-off and landing

This paper employs the Chan-Vese (CV) model into aircraft objective extraction for binocular stereo vision to enable autonomous take-off and landing of unmanned aerial vehicles. Fundamental principles of the CV model and the level set method are summarized as minimizing energy function. Eventually, a flying UAV objective extraction algorithm is proposed and developed by using the CV model. Two sets of UAV landing images are collected for validation. Experimental results show that the proposed algorithm can effectively extract the UAV target even with a complex background. Furthermore, the accuracy of localization is comparable with DGPS and it is better than that BRISK maximal response value algorithm.