Autonomous vehicles are dependent on High Definition (HD) maps. The process of generating and updating these maps is slow, expensive, and not scalable for the whole world. Crowdsourcing vehicle sensor data to generate and update maps is a solution to the problem. In this paper, we propose and evaluate an end-to-end pose-graph optimization-based mapping framework using crowdsourced vehicle data. The in-vehicle data acquisition framework and the cloud-based mapping framework that fuses data from a consumer-grade Global Navigation Satellite System (GNSS) receiver, an odometry sensor, and a stereo camera is described in detail. We focus on using stereo image pairs for loop-closure detection to combine crowdsourced data from different sessions that are affected by GNSS biases. We evaluate our framework on a data-set of more than 180 km recorded around the Eindhoven area. After the map generation process, the results exhibit a 56.23% improvement in maximum offset error and a 24.39% improvement in precision around the loop-closure area.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pose-graph based Crowdsourced Mapping Framework


    Beteiligte:


    Erscheinungsdatum :

    2020-11-01


    Format / Umfang :

    6860935 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Mapping ridership using crowdsourced cycling data

    Jestico, Ben | Online Contents | 2016


    Mapping ridership using crowdsourced cycling data

    Jestico, Ben / Nelson, Trisalyn / Winters, Meghan | Elsevier | 2016


    Bayesian Framework for Vehicle Localization Using Crowdsourced Data

    Verentsov, Sergey / Magerramov, Emil / Vinogradov, Vlad et al. | IEEE | 2018


    BAYESIAN FRAMEWORK FOR VEHICLE LOCALIZATION USING CROWDSOURCED DATA

    Verentsov, Sergey / Magerramov, Emil / Vinogradov, Vlad et al. | British Library Conference Proceedings | 2018


    Generalized model for mapping bicycle ridership with crowdsourced data

    Nelson, Trisalyn / Roy, Avipsa / Ferster, Colin et al. | Elsevier | 2021