Object Detection with Deep Learning to Accelerate Pose Estimation for Automated Aerial Refueling

Remotely piloted aircraft (RPAs) cannot currently refuel during flight because the latency between the pilot and the aircraft is too great to safely perform aerial refueling maneuvers. However, an AAR system removes this limitation by allowing the tanker to directly control the RP A. The tanker quickly finding the relative position and orientation (pose) of the approaching aircraft is the first step to create an AAR system. Previous work at AFIT demonstrates that stereo camera systems provide robust pose estimation capability. This thesis first extends that work by examining the effects of the cameras' resolution on the quality of pose estimation. Next, it demonstrates a deep learning approach to accelerate the pose estimation process. The results show that this pose estimation process is precise and fast enough to safely perform AAR.