Abstract:

Considering the space tumbling non-cooperative target motion uncertainty and without any priori knowledge, a relative pose estimation method based on vision-only simultaneous localization and mapping(SLAM) for space tumbling non-cooperative target is proposed. Firstly, the state transition equation and measurement update equation of Bayes filter model for relative pose estimation is established. Secondly, in order to overcome the detrimental effects caused by large noise covariance of translation process model, the least square estimate is adopted to predict the position parameter and a improved process model is obtained. Finally, a fast and smooth relative pose estimation is realized by using a Bayes filter combining unscented Kalman filter(UKF) and particle filter(PF). Results from both synthetic and real image sequences show that the method is accurate and efficient, and is robust to the changeful target rotation velocity and the errors of feature extraction and data association.

Key words: Tumbling noncooperative target, Relative pose estimation, SLAM, Bayes filter