Landing Area Detection Using Drone Aerial Images Based on Ground Segmentation and Dense Optical Flow

In order to realize autonomous drones that collect information and transport supplies by air, it is required to automatically detect a safe landing site in an unknown environment. In this paper, we propose a method to find a candidate landing site using ground images captured by a monocular camera from a drone in flight. The proposed method evaluates the safety of the ground surface by combining the surface classification through Semantic Segmentation and the flatness estimation from dense optical flow. The evaluation is performed for each pixel of the captured images, and a detailed shape of the possible landing area can be obtained. We applied the method to actual images taken by a drone and verified that the landable area was extracted from an altitude of about 100 meters.