LAUSR

Abstract Acquiring the relative pose between an uncooperative target and a chaser satellite poses a unique problem for any active debris removal mission. A lack of prior knowledge regarding the target's motion, mass distribution and shape limits the possibilities for accurately tracking the target. In this paper, a stereo-camera pair, mounted on a chaser satellite, is used to extract unique features on the surface of an unknown, uncooperative target using the scale invariant feature transform (SIFT). The features are used as measurement input to an extended Kalman Filter (EKF) that makes use of the simultaneous localisation and mapping (SLAM) approach. The orientation and position of the target relative to the chaser is estimated, along with the target's linear and angular velocities. This motion is estimated relative to the camera reference frame (CRF) while the system simultaneously calculates the shape and size of the target. A simulation environment is created to test and verify the estimation algorithm. The integration of the feature extractor with the EKF is tested using real camera data. A laboratory experiment was conducted to capture image sequences of a moving target. A number of practical considerations is highlighted when such a system is to be applied to a mission. Results show that stereo cameras, along with the EKF-SLAM approach is a viable method for fully autonomous space debris mitigation systems.