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Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter
Authors:Yan Xu  Delin Luo  Ning Xian  Haibin Duan
Institution:1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, PR China;2. School of Information Science and Technology, Xiamen University, Xiamen 361005, PR China
Abstract:In recent years, many pose estimation algorithms were developed, and have been successfully applied to solve unmanned aerial vehicle (UAV) aerial refueling pose estimation problems. This paper mainly focuses on solving this problem under serious turbulences circumstance. The extended Kalman filter is a set of mathematical equations to estimate the state of a process, which is able to support estimations of past, present, and even future states. In reference to previous papers and some simulations, we build up the noise models of refueling boom and atmospheric turbulence. Then, an extend Kalman filter is adopted to solve the pose estimation problem in UAV aerial refueling with serious turbulences. The experimental results demonstrate the feasibility and effectiveness of our proposed approach.
Keywords:Unmanned aerial vehicle (UAV)  Aerial refueling  Machine vision  Pose estimation  Extend Kalman filter
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