This paper describes a vision-based sense-and-avoid framework to detect approaching aircraft especially observed with cluttered background. The proposed framework consists of a vision system with a camera that processes the incoming images using a series of algorithms in real time to isolate moving aerial objects on the image plane and classify them using a particle filter. Once an approaching aerial object has been detected on a potential collision course, the aircraft performs an evasive maneuver. The performance of the proposed sense-and-avoid algorithm is validated in a series of test flights using two unmanned aerial vehicles.


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    Titel :

    Vision-based sense-and-avoid framework for unmanned aerial vehicles


    Beteiligte:
    Sungsik Huh, (Autor:in) / Sungwook Cho, (Autor:in) / Yeondeuk Jung, (Autor:in) / Shim, David Hyunchul (Autor:in)


    Erscheinungsdatum :

    2015-10-01


    Format / Umfang :

    2315210 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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