In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver Ego spacecraft to track the uncooperative Target.


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    Titel :

    Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation


    Beteiligte:

    Erscheinungsdatum :

    2022-01-01


    Anmerkungen:

    Becktor , J B , Seto , W , Deole , A , Bandyopadhyay , S , Rahimi , N , Talebi , S , Mesbahi , M & Rahmani , A 2022 , Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation . in Proceedings of 2022 IEEE Aerospace Conference . IEEE , 43 rd IEEE Aerospace Conference 2022 , Big Sky , Montana , United States , 05/03/2022 . https://doi.org/10.1109/AERO53065.2022.9843396



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629



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