Abstract
In this work, a study on computer vision techniques for automating rendezvous manoeuvres in space has been carried out. A lightweight algorithm pipeline for achieving the 6 degrees of freedom (DOF) object pose estimation, i.e. relative position and attitude, of a spacecraft in a non-cooperative context using a monocular camera has been studied. In particular, the considered lite architecture has been never exploited for space operations and it allows to be compliant with operational constraints, in terms of payload and power, of small satellite platforms. Experiments were performed on a benchmark Satellite Pose Estimation Dataset of synthetic and real spacecraft imageries specifically introduced for the challenging task of the 6DOF object pose estimation in space. Extensive comparisons with existing approaches are provided both in terms of reliability/accuracy and in terms of model size that ineluctably affect resource requirements for deployment on space vehicles.
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This work was supported in part by the Ministry of Education, University and Research under the grant PM3 AER01_01181 Modular Multi-Mission Platform.
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Carcagnì, P., Leo, M., Spagnolo, P., Mazzeo, P.L., Distante, C. (2022). A Lightweight Model for Satellite Pose Estimation. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_1
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