Collaborative Pose Estimation of an Unknown Target Using Multiple Spacecraft

A reliable method for estimating the pose of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination is challenging due to factors such as harsh lighting conditions, rotational dynamics of the target, and scale ambiguity of the monocular camera. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi-Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS. Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its images and extracts its own set of visual keypoints in parallel. Then, the team uses the local network to jointly estimate the target pose and shape in a distributed fashion by applying the consensus algorithm over the inter-spacecraft communication links. We validate our algorithm using simulations of relative orbits and observations captured by each chaser spacecraft. To the best of the authors' knowledge, this is the first cooperative vision-based algorithm for estimating the pose and shape of a space object by means of an arbitrary number of spacecraft.