Abstract In current vehicles, redundant sensors with heterogeneous measurement principles are applied in increasing numbers. Taking the advantage of these already existing redundancies, the concept of a central virtual sensor for the estimation of kinematic vehicle properties is created, based on a set of close-to-series sensors, consisting of a MEMS inertial measurement unit, a GPS receiver, and odometry sensors. Furthermore, a real-time capable implementation of this architecture is realized, using a linearized Error-State-Space Kalman filter. This fusion filter is enhanced by a correction algorithm for measurement latencies of multiple sensors and a two-step plausibilization of raw measurement data. In addition, an integrated assessment of the data quality is implemented. It describes data consistency using an integrity measure and data accuracy with a virtual datasheet.


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    Title :

    Data Fusion for Precise Localization


    Contributors:


    Edition :

    2016


    Publication date :

    2015-12-08


    Size :

    42 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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