<title>Pose determination of spinning satellites using tracks of novel regions</title>

Abstract

An algorithm for using time sequence video data from a single camera to determine position and orientation (pose) of spin stabilized satellites with respect to a robotic spacecraft is discussed. The system utilizes novelty detection and filtering for locating novel parts and a neural net to track these parts over time. The present paper addresses the estimation of pose from the tracks of the novel regions. The path traced out by a given part (or region) is approximately elliptical in image space, and a psuedoinverse technique is used to find a best-fit ellipse for a set of track points. The position, shape, and orientation of the ellipse are functions of the satellite geometry and its pose. Using this ellipse, and information from a model of the given satellite, an iterative technique is used to perturb an initial guess of pose such that the error between the best-fit ellipse and a predicted ellipse is minimized. Results of using this algorithm on sequences of images of a satellite at various poses and under various lighting conditions are presented.