Stable Pose Estimation with a Motion Model in Real-Time Application

Estimation of a object pose from camera is a well-developing topic in computer vision. In theory, the pose from a calibrated camera can be uniquely determined. But in practice, most of the real-time pose estimation algorithms suffer from pose ambiguity due to low accuracy of the target object. We think that pose ambiguity¡Xtwo distinct local minima of the according error function¡Xexist because of the phenomenon of geometric illusions. Both of the ambiguous poses are plausible. After obtaining the solution of two minima (pose candidates), we develop a real-time algorithm for stable pose estimation of a target objects with a motion model. In the experimental results, the proposed algorithm diminish the significance of pose jumping and pose jittering effectively. To the best of our knowledge, this is the first work to solve the pose ambiguity problem with motion model in real-time application.