Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation Challenge aims at evaluating and comparing monocular-vision-based approaches and pushing the state of the art on this problem. This article is based on the Satellite Pose Estimation Dataset, the first publicly available machine learning set of synthetic and real spacecraft imageries. The choice of dataset reflects one of the unique challenges associated with spaceborne computer vision tasks, namely the lack of spaceborne images to train and validate the developed algorithms. This article briefly reviews the basic properties and the collection process of the dataset that was made publicly available. The competition design, including the definition of performance metrics and the adopted testbed, is also discussed. The main contribution of this article is the analysis of the submissions of the 48 competitors, which compares the performance of different approaches and uncovers what factors make the satellite pose estimation problem especially challenging.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results


    Beteiligte:
    Kisantal, Mate (Autor:in) / Sharma, Sumant (Autor:in) / Park, Tae Ha (Autor:in) / Izzo, Dario (Autor:in) / Martens, Marcus (Autor:in) / Damico, Simone (Autor:in)


    Erscheinungsdatum :

    2020-10-01


    Format / Umfang :

    4073432 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Satellite Pose Estimation Competition 2021: Results and Analyses

    Park, Tae Ha / Märtens, Marcus / Jawaid, Mohsi et al. | Elsevier | 2023


    Fast Dataset Collection Approach for Articulated Equipment Pose Estimation

    Liang, Ci-Jyun / Lundeen, Kurt M. / McGee, Wes et al. | ASCE | 2019



    Revisiting Monocular Satellite Pose Estimation With Transformer

    Wang, Zi / Zhang, Zhuo / Sun, Xiaoliang et al. | IEEE | 2022


    A Lightweight Model for Satellite Pose Estimation

    Carcagnì, Pierluigi / Leo, Marco / Spagnolo, Paolo et al. | British Library Conference Proceedings | 2022