  In real-world applications the Perspective-n-Point (PnP) problem should
generally be applied in a sequence of images which a set of drift-prone
features are tracked over time. In this paper, we consider both the temporal
dependency of camera poses and the uncertainty of features for the sequential
camera pose estimation. Using the Extended Kalman Filter (EKF), a priori
estimate of the camera pose is calculated from the camera motion model and then
corrected by minimizing the reprojection error of the reference points.
Experimental results, using both simulated and real data, demonstrate that the
proposed method improves the robustness of the camera pose estimation, in the
presence of noise, compared to the state-of-the-art.
