The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.


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    Titel :

    Probabilistic localization method for trains


    Beteiligte:


    Erscheinungsdatum :

    2012-06-01


    Format / Umfang :

    498348 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Probabilistic Localization Method for Trains

    Heirich, O. / Robertson, P. / Cardalda Garcia, A. et al. | British Library Conference Proceedings | 2012



    Localization of trains and mapping of railway tracks

    Heirich, Oliver / Technische Universität München / Verlag Dr. Hut, München | TIBKAT | 2020