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

A Lightweight Model for Satellite Pose Estimation

Carcagnì Pierluigi
Primo
Membro del Collaboration Group
;
Leo Marco
Secondo
Membro del Collaboration Group
;
Spagnolo Paolo
Penultimo
Membro del Collaboration Group
;
Mazzeo Pier Luigi
Membro del Collaboration Group
;
Distante Cosimo
Ultimo
Membro del Collaboration Group
2022

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
2022
Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" - ISASI - Sede Secondaria Lecce
6DOF pose, Deep learning, Monocular vision, Space imagery, Spacecraft pose estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/539867
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