This article describes a method to develop a generic approach to acquire navigation capabilities for the standard platform of the IMAV indoor competition: the Parrot AR.Drone. Our development is partly based on simulation, which requires both a realistic sensor and motion model. The AR.Drone simulation model is described and validated. Furthermore, this article describes how a visual map of the indoor environment can be made, including the effect of sensor noise. This visual map consists of a texture map and a feature map. The texture map is used for human navigation and the feature map is used by the AR.Drone to localize itself. To do so, a localization method is presented. An experiment demonstrates how well the localization works for circumstances encountered during the IMAV competition.


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    Titel :

    Integrating Sensor and Motion Models to Localize an Autonomous AR.Drone


    Beteiligte:
    Dijkshoorn, Nick (Autor:in) / Visser, Arnoud (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2011-12-01


    Format / Umfang :

    18 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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