This paper presents a novel approach for aerial drone autonomous navigation along predetermined paths using only visual input form an onboard camera and without reliance on a Global Positioning System (GPS). It is based on using a deep Convolutional Neural Network (CNN) combined with a regressor to output the drone steering commands. Furthermore, multiple auxiliary navigation paths that form a ‘navigation envelope’ are used for data augmentation to make the system adaptable to real-life deployment scenarios. The approach is suitable for automating drone navigation in applications that exhibit regular trips or visits to same locations such as environmental and desertification monitoring, parcel/aid delivery and drone-based wireless internet delivery. In this case, the proposed algorithm replaces human operators, enhances accuracy of GPS-based map navigation, alleviates problems related to GPS-spoofing and enables navigation in GPS-denied environments. Our system is tested in two scenarios using the Unreal Engine-based AirSim [32] plugin for drone simulation with promising results of average cross track distance less than 1.4 meters and mean waypoints minimum distance of less than 1 meter.


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    Titel :

    Deep convolutional neural network based autonomous drone navigation


    Beteiligte:
    Amer, Karim (Autor:in) / Samy, Mohamed (Autor:in) / Shaker, Mahmoud (Autor:in) / ElHelw, Mohamed (Autor:in)

    Kongress:

    Thirteenth International Conference on Machine Vision ; 2020 ; Rome,Italy


    Erschienen in:

    Proc. SPIE ; 11605


    Erscheinungsdatum :

    04.01.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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