This paper describes a novel stereo vision sensor based on deep neural networks, that can be used to produce a feedback signal for visual servoing in unmanned aerial vehicles such as drones. Two deep convolutional neural networks attached to the stereo camera in the drone are trained to detect wind turbines in images and stereo triangulation is used to calculate the distance from a wind turbine to the drone. Our experimental results show that the sensor produces data accurate enough to be used for servoing, even in the presence of noise generated when the drone is not being completely stable. Our results also show that appropriate filtering of the signals is needed and that to produce correct results, it is very important to keep the wind turbine within the field of vision of both cameras, so that both deep neural networks could detect it.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Deep Neural Network Sensor for Visual Servoing in 3D Spaces


    Beteiligte:

    Erscheinungsdatum :

    2020-03-01


    Anmerkungen:

    Durdevic , P & Ortiz Arroyo , D 2020 , ' A Deep Neural Network Sensor for Visual Servoing in 3D Spaces ' , Sensors , vol. 20 , no. 5 , 1437 . https://doi.org/10.3390/s20051437



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    A bayesian deep neural network for safe visual servoing in human–robot interaction

    Shi, Lei / Copot, Cosmin / Vanlanduit, Steve | BASE | 2021

    Freier Zugriff

    Visual Servoing Systems

    Copot, Cosmin / Ionescu, Clara Mihaela / Muresan, Cristina I. | Springer Verlag | 2020


    Visual servoing - Nonlinear observer approach

    Hashimoto, K. / Kimura, H. | British Library Online Contents | 1995


    TUDOR initial visual servoing experiments

    Malzahn, Jörn / Phung, Anh Son / Hoffmann, Frank et al. | TIB AV-Portal | 2011


    Visual Servoing of Legged Robots

    Echegoyen, Z. | British Library Online Contents | 2012