Detecting damage to bridges is essential for safety and financial reasons. We focus on inspections inside bridges and claim Unmanned Aerial Vehicles (UAVs) are ideally suited to assist inspections of hard-to-reach areas. Using UAVs in narrow indoor environments imposes constraints on the size of the UAV. While small UAVs are attractive for their agility and reduced risks associated with them, the need to carry cameras and sensors results in a lower bound on thrust and thus UAV and rotor size. Moreover, position and orientation control is particularly demanding inside bridges because satellite navigation is not available. To address these challenges, we implement a nano UAV that is capable of analyzing camera data for cracks with a machine learning model. UAV position is tracked with inertial measurement units, an optical flow, and a laser-based range sensor.


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    Titel :

    Inside Bridges: Autonomous Crack Inspection with Nano UAVs in GNSS-Denied Environments


    Beteiligte:
    Muller, David (Autor:in) / Herbers, Patrick (Autor:in) / Dyrska, Raphael (Autor:in) / Celik, Firdes (Autor:in) / Konig, Markus (Autor:in) / Monnigmann, Martin (Autor:in)


    Erscheinungsdatum :

    12.12.2024


    Format / Umfang :

    3507496 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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    Freier Zugriff

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