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In real-world applications the Perspective-n-Point (PnP) problem shouldgenerally be applied in a sequence of images which a set of drift-pronefeatures are tracked over time. In this paper, we consider both the temporaldependency of camera poses and the uncertainty of features for the sequentialcamera pose estimation. Using the Extended Kalman Filter (EKF), a prioriestimate of the camera pose is calculated from the camera motion model and thencorrected by minimizing the reprojection error of the reference points.Experimental results, using both simulated and real data, demonstrate that theproposed method improves the robustness of the camera pose estimation, in thepresence of noise, compared to the state-of-the-art.

EKFPnP: Extended Kalman Filter for Camera Pose Estimation in a Sequence of Images