Automatically Landing an Unmanned Aerial Vehicle Using Perspective-n-Point Algorithm Based on Known Runway Image: Area Localization and Feature Enhancement With Time Consumption Reduction

This research proposes a method to track a known runway image to land an unmanned aerial vehicle (UAV) automatically by finding a perspective transform between the known image and an input image in real-time. Apparently, it improves the efficiency of feature detectors in real-time, so they can better respond to perspective transformation and reduce the processing time. A UAV is an aircraft that is controlled without a human pilot on board. The flight of a UAV operates with various degrees of autonomy, either autonomously using computational-limited on-board computers or under remote control by a human operator. UAVs were originally applied for missions where human access was not readily available or where it was dangerous for humans to go. Nowadays, the most important problem in monitoring by an autopilot is that the conventional system using only the GPS sensors provides inaccurate geographical positioning. Therefore, controlling the UAV to take off from or land on a runway needs professional input which is a scarce resource. The characteristics of the newly developed method proposed in this paper are: (1) using a lightweight feature detector, such as SIFT or SURF, and (2) using the perspective transformation to reduce the effect of affine transformation that results in the feature detector becoming more tolerant to perspective transformation. In addition, the method is also capable of roughly localizing the same template in consecutive frames. Thus, it limits the calculation area that feature matching needs to work on.