In this paper, we propose a deep neural-network based regression approach, combined with a 3D structure based computer vision method, to solve the relative camera pose estimation problem for autonomous navigation of UAVs. Different from existing learning-based methods that train and test camera pose estimation in the same scene, our method succeeds in estimating relative camera poses across various urban scenes via a single trained model. We also built a Tuebingen Buildings database of RGB images collected by a drone in eight urban scenes. Over 10,000 images with corresponding 6DoF poses as well as 300,000 image pairs with their relative translational and rotational information are included in the dataset. We evaluate the accuracy of our method in the same scene and across scenes, using the Cambridge Landmarks dataset and the Tuebingen Buildings dataset. We compare the performance with existing learning-based pose regression methods PoseNet and RPNet on these two benchmark datasets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    RCPNet: Deep-Learning based Relative Camera Pose Estimation for UAVs


    Beteiligte:
    Yang, Chenhao (Autor:in) / Liu, Yuyi (Autor:in) / Zell, Andreas (Autor:in)


    Erscheinungsdatum :

    01.09.2020


    Format / Umfang :

    1080279 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Equivalent Spatial Plane-Based Relative Pose Estimation of UAVs

    Hangyu Wang / Shuangyi Gong / Chaobo Chen et al. | DOAJ | 2024

    Freier Zugriff


    Camera-Based Pose Estimation for Fixed-Wing UAVs During Cooperative Landing Maneuvers

    Hebisch, Christoph / Jackisch, Sven / Moormann, Dieter et al. | British Library Conference Proceedings | 2022


    Deep Learning-Based Covariance Estimation for Relative Pose Measurements

    Ahrabian, Alireza / Nguyen, Quan / Toulios, Nikos et al. | IEEE | 2024


    FPGA Hardware Acceleration for Deep Learning-Based Satellite Relative Pose Estimation

    Capuano, Giovanni Maria / Capuano, Vincenzo / Napolano, Giuseppe et al. | AIAA | 2025