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A novel approach to position and orientation estimation for vision-based UAV (unmanned aerial vehicle) navigation is described. In this approach the position and orientation estimation problem is formulated as a tracking problem and solved by using an extended Kalman filter (EKF). The state and observation models of the EKF are established based on an analysis of the imaging geometry of the UAV's video camera in connection with a DEM (digital elevation map) of the area of flight, which helps to control estimation error accumulation. The efficacy of our approach is demonstrated by simulation experiment results.
Zhang et al. (Thu,) studied this question.