Remote Satellite Position & Pose Estimation Using Monocular Vision

This thesis investigates methods for estimating relative 3D position and pose from monocular image sequences. The intended future application is of one satellite observing another, when flying in close formation. The ideas explored in this thesis build on methods developed for use in camera calibration and Kalman filter-based structure from motion (SfM). Each of the algorithms relies on visible feature points affixed to the target satellite with known geometry. To the author’s knowledge, monocular vision in a Kalman filter milieu has not been previously used to estimate satellites’ relative position and orientation. After describing the problem from a mathematical perspective we develop different approaches to solving the estimation problem. The different approaches are successfully tested on simulated as well as real-world image sequences, and their performance analyzed. Results show the algorithms to be successful in tracking simulated as well as physical targets. The effectiveness of a direct least-squares solution versus a stand-alone Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) is investigated. The recently developed Unscented Kalman Filter is found to be less suited to our application than the more widely used EKF.