Autonomous rendezvous and approaching of spacecrafts and uncooperative space objects is the fundamental portion of future on-orbit satellite maintenance or asteroid exploration mission, while estimating the relative attitude and position of known uncooperative space objects on-board is still one of challenges in these tasks. Recently, the development of flash light detection and ranging sensors (LIDARs) and 3D imaging technology provides a feasible and reliable method for relative navigation. This paper proposes an end-to-end neural network based on Transformer to estimate 6-DoF attitude of the uncooperative space object with point cloud. The experiments are conducted to validate the performance and robustness of our network.


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    Title :

    6-DoF Pose Estimation of Uncooperative Space Object Using Deep Learning with Point Cloud


    Contributors:
    Zhang, Shaodong (author) / Hu, Weiduo (author) / Guo, Wulong (author)


    Publication date :

    2022-03-05


    Size :

    7303475 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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