EKFPnP: extended Kalman filter for camera pose estimation in a sequence of images

In real‐world applications the perspective‐n‐point (PnP) problem should generally be applied to a sequence of images which a set of drift‐prone features are tracked over time. In this study, the authors consider both the temporal dependency of camera poses and the uncertainty of features for the vision‐only 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 it is corrected by minimising the reprojection error of the reference points. Applying probabilistic approach also provides the covariance of the pose parameters which helps to measure the reliability of the estimated parameters. Experimental results, using both synthetic and real data, demonstrate that the proposed method improves the robustness of the camera pose estimation, in the presence of tracking error and feature matching outliers, compared to the state of the art.