Landing Site Inspection and Autonomous Pose Correction for Unmanned Aerial Vehicles

Abstract

Large number of disturbances and uncertainties in the environment makes landing one of the tricky maneuvers in all the phases of flying an unmanned aerial vehicle. The situation even worsens at the time of emergencies. To allow safe landing of the UAVs on rough terrains with a lot of ground objects, an automatic landing site inspection and real-time pose correction system while landing is in demand in current world situation. This paper presents a method of detection of designated landing sites and autonomously landing in a safe environment. The airborne vision system is utilized with fully convolution neural network to recognize the landing markers on the landing site and object detection. Automatic pose correction algorithm is developed to position the drone for landing in a safe zone and as near to the landing marker as possible. The information from the onboard visual sensors and Inertial Measurement Unit (IMU) is utilized to estimate pose for the perfect landing trajectory. A series of experiments are presented to test and optimize the proposed method.