3D point tracking and pose estimation of a space object using stereo images

In this paper, we present a novel method for stereo camera-based 3D tracking which integrates point-kinematics, associated to each visual feature into Kalman filters. The approach utilizes optical flow and stereo correspondence of visible, predominatly specular features on a target satellite surface, in order to estimate translational and rotational velocities of the rigid body. The motion of each 3D point cloud can be predicted, since all point clouds are constrained by the common, rigid motion. A dual quaternion-based pose estimator, robustified with median statistics, is further applied to the estimated points. In case of temporarily missing measurements, the last estimated body velocity is used to predict the next poses. Experimental results based on images of a satellite simulator are shown to demonstrate performances for on-orbit servicing.