This paper presents a cooperative unmanned aerial vehicle navigation algorithm that allows a chief vehicle (equipped with inertial and magnetic sensors, a Global Positioning System receiver, and a vision system) to improve its navigation performance (in real time or in postprocessing phase), exploiting line-of-sight measurements from formation-flying deputies equipped with Global Positioning System receivers. The key concept is to integrate differential Global Positioning System and visual tracking information within a sensor fusion algorithm based on the extended Kalman filter. The developed concept and processing architecture are described, with a focus on the filtering algorithm. Then, flight-testing strategy and experimental results are presented. In particular, cooperative navigation output is compared with the estimates provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit accurate magnetic- and inertial-independent information.


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    Title :

    Satellite and Vision-Aided Sensor Fusion for Cooperative Navigation of Unmanned Aircraft Swarms


    Contributors:

    Published in:

    Publication date :

    2017-06-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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