This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving depth information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots.


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    Titel :

    Real-time gesture control UAV with a low resource framework


    Beteiligte:
    Bhat, Manohar (Autor:in) / Mahto, Gopikishan (Autor:in) / Kesaria, Smit (Autor:in) / Femandes, Vikrant (Autor:in) / Arya, Kavi (Autor:in)


    Erscheinungsdatum :

    20.09.2021


    Format / Umfang :

    586455 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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