This paper presents a system that integrates Invariant Extended Kalman Filters with onboard sensors and map-based corrections. The system combines multiple sensors, such as an Inertial Measurement Unit and a speedometer, to improve accuracy and robustness during gaps in the map. The proposed architecture takes into consideration the different nature of each measurement used during the update step, such as map-based measurements in the world-frame and speedometer measurements in the body-frame. The experimental evaluations were conducted in diverse driving conditions. The proposed system consistently outperformed traditional solutions based on extended Kalman filters, sustaining low relative translational errors during map outages.


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    Titel :

    Robust Land Vehicle Positioning with Invariant Kalman Filters and Map-Based Corrections




    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    8070087 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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