Autonomous Detection of Safe Landing Areas for an UAV from Monocular Images

This paper presents an approach to detect safe landing areas for a flying robot, on the basis of a sequence of monocular images. The approach does not require precise position and attitude sensors: it exploits the relations between 2D image homographies and 3D planes. The combination of a robust homography estimation and of an adaptive thresholding of correlation scores between registered images yields the update of a stochastic grid, that exhibits the horizontal planar areas perceived. This grid allows the integration of data gathered at various altitudes. Results are presented