Noncooperative spacecraft pose estimation plays a crucial role in on-orbit servicing. However, existing pose estimation methods often assume CAD models of target objects as prior information, used for offline training or online template matching. This limits the generalization of pose estimation methods. To explore a generic solution, this work proposes a pose estimation method for unknown spacecraft. Our method is not only independent of prior models or image priors of the target but also synchronously outputs pose parameters and aligned target texture models. Specifically, we employ three modules in parallel: pose tracking, neural object reconstruction, and target reference frame (TRF) estimation. First, leveraging the knowledge of temporal data, we optimize the pose graph to provide stable tracking performance. Then, we use neural implicit representation to reconstruct the target texture model, with pose parameters jointly optimized during the reconstruction process. Finally, we propose TRFE-Net for online estimation of the TRF. The obtained TRF is used to correct the sensor reference frame, transforming the pose tracking and reconstruction problem from scene-centric to spacecraft-centric. In addition, the PEU dataset was constructed specifically for pose estimation of unknown spacecraft. Comprehensive experiments show that although the proposed method reduces the need for prior information, it still achieves good performance across multiple objects and effectively handles large-scale motions, specular highlights, thin structures, and symmetric structures.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pose Estimation and Neural Implicit Reconstruction Toward Noncooperative Spacecraft Without Offline Prior Information


    Beteiligte:
    Han, Bing (Autor:in) / Wang, Chenxi (Autor:in) / Zhang, Xinyu (Autor:in) / Zhao, Zhibin (Autor:in) / Zhai, Zhi (Autor:in) / Liu, Jinxin (Autor:in) / Liu, Naijin (Autor:in) / Chen, Xuefeng (Autor:in)


    Erscheinungsdatum :

    01.04.2025


    Format / Umfang :

    12090767 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Pose and Shape Reconstruction of a Noncooperative Spacecraft Using Camera and Range Measurements

    Renato Volpe / Marco Sabatini / Giovanni B. Palmerini | DOAJ | 2017

    Freier Zugriff

    Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft (AAS 19-840)

    Park, Tae Ha / Sharma, Sumant / D'Amico, Simone | TIBKAT | 2020


    Position Awareness Network for Noncooperative Spacecraft Pose Estimation Based on Point Cloud

    Liu, Xiang / Wang, Hongyuan / Chen, Xinlong et al. | IEEE | 2023