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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    Title :

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




    Publication date :

    2023-09-24


    Size :

    8070087 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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