In an environment of escalating usage of Low Earth Orbit, the active remediation of debris is an increasingly necessary capability. Computer vision pose estimation is a core competency of active debris remediation but state-of-the-art pose estimation methods continue to grow in size and complexity. For bandwidth limited and edge computing cases, smaller networks are more feasibly implemented. A 16,845 synthetic image dataset, applicable to the upcoming JAXA CRD2 project, is rendered and a small pose estimation network is constructed and trained on the dataset. The network is then quantized, reducing the memory requirement by a factor of 8x to a theoretical size of 5.5 MB. The 5.5 MB network demonstrates sufficient accuracy in both single image pose and motion prediction tasks when compared to the full precision 32 bit network.


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    Titel :

    Render-to-real image dataset and CNN pose estimation for down-link restricted spacecraft missions


    Beteiligte:
    Price, Andrew (Autor:in) / Uno, Kentaro (Autor:in) / Parekh, Swapnil (Autor:in) / Reichelt, Til (Autor:in) / Yoshida, Kazuya (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    04.03.2023


    Format / Umfang :

    21596203 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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