A Convolutional Neural Network (CNN) vision-based approach is demonstrated to enable autonomous flight of a stock unmodified quadrotor drone in hallway environments. The video stream from a monocular front-facing camera on-board a quadrotor drone is fed to Convolutional Neural Network (CNN) environment classifiers at a base station in order to detect upcoming intersections and dead-ends. Detecting these hallway structural features allows our control planning algorithms to take appropriate action in order to stop and turn at intersections or stop before colliding with dead-ends such as walls and doors. The use of CNNs permit intersections and dead-ends to be detected with a high degree of accuracy in a wide variety of indoor environments with varying contrasts, lighting conditions, obstructions, and many other conditions that prevent easy generalization of feature extraction. Overall, our approach allows for real-time navigation at high rates of speed approaching 2 m/s.


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    Titel :

    A Convolutional Neural Network Vision System Approach to Indoor Autonomous Quadrotor Navigation


    Beteiligte:
    Garcia, Adriano (Autor:in) / Mittal, Sandeep S. (Autor:in) / Kiewra, Edward (Autor:in) / Ghose, Kanad (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    451630 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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