The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics. In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.


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    Title :

    Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles


    Contributors:

    Publication date :

    2020-01-01


    Remarks:

    XXIV ISPRS Congress, Commission II : edition 2020 ; ISPRS Archives ; 43,B2



    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    550 / 629





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