Vision-based Pose and Motion Estimation of Non-cooperative Target for Space Robotic Manipulators

Abstract

This paper developed a real-time vision-based pose and motion estimation of noncooperative target by extended Kalman filter. Optical flow algorithm was adopted to track the feature points of the target in order to increase the imaging processing speed. In addition, photogrammetry was used to provide more accurate initial conditions as well as accelerate the convergence rate of the extended Kalman filter. The proposed scheme has been implemented by a single camera to estimate the pose and motion of a static and dynamic target experimentally. The estimated results were compared with that of photogrammetry and demonstrated that the proposed scheme is robust and accurate in pose and motion estimation from noisy images.