Helipad detection for accurate UAV pose estimation by means of a visual sensor

In this article, we tackle the problem of developing a visual framework to allow the autonomous landing of an unmanned aerial vehicle onto a platform using a single camera. Specifically, we propose a vision-based helipad detection algorithm in order to estimate the attitude of a drone on which the camera is fastened with respect to target. Since the algorithm should be simple and quick, we implemented a method based on curvatures in order to detect the heliport marks, that is, the corners of character H. By knowing the size of H mark and the actual location of its corners, we are able to compute the homography matrix containing the relative pose information. The effectiveness of our methodology has been proven through controlled indoor and outdoor experiments. The outcomes have shown that the method provides high accuracies in estimating the distance and the orientation of camera with respect to visual target. Specifically, small errors lower than 1% and 4% have been achieved in the computing of measurements, respectively.

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