Improving Automated Aerial Refueling Stereo Vision Pose Estimating Using a Shelled Reference Model
Abstract
Automated Aerial Refueling of Unmanned Aerial Vehicles is vital to the United States Air Force's continued air superiority. This research presents a novel solution for computing a relative 6 degree-of-freedom pose between the refueling aircraft and a tanker. The approach relies on a real time 3D virtual simulation environment that models a realistic refueling scenario. Synthetic imagery is processed by computer vision algorithms that calculate the sensed relative-navigation position and orientation. Pose estimation accuracy and computational speed during registration improve though the use of a shelled reference model. The shelled model improves computational speed of pose estimation at the refueling position by 87 and accuracy by 36 when compared with a full reference model. To ensure proper simulation of computer vision concepts, this research quantifies the effect Multi-Sample Anti Aliasing implemented in the virtual stereo cameras on camera calibration and pose estimation. A combined shelled model and Multi-Sample Anti Aliased approach leads to position estimation errors less then 7cm and orientation estimation error less than 1.