Learning what is above and what is below: horizon approach to monocular obstacle detection

Abstract: A novel approach is proposed for monocular obstacle detection, which relies on self-supervised learning to discriminate everything above the horizon line from everything below. Obstacles on the path of a robot that keeps moving at the same height, will appear both above and under the horizon line. This implies that classifying obstacle pixels will be inherently uncertain. Hence, in the proposed approach the classifier's uncertainty is used for obstacle detection. The (preliminary) results show that this approach can indeed work in different environments. On the well-known KITTI data set, the self-supervised learning scheme clearly segments the road and sky, while application to a flying data set leads to the segmentation of the flight arena's floor.