Simultaneous localization and mapping (SLAM) has been an emerging research topic in the fields of robotics, autonomous driving, and unmanned aerial vehicles over the past thirty years. State of the art SLAM research is often inaccessible for undergraduate student researchers due to expensive hardware and difficult software setup. We present a cost-friendly vehicle research platform and a robust implementation of SLAM. Our SLAM algorithm fuses visual stereo image and 2D light detection and ranging (Lidar) data and uses loop closure for accurate odometry estimation. Our algorithm is benchmarked against other popular SLAM algorithms using the publicly available KITTI dataset and shown to be very accurate. For educational purposes, we publicly share the models and code presented in this work*.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Multisensor Data Fusion Approach for Simultaneous Localization and Mapping*


    Contributors:


    Publication date :

    2019-10-01


    Size :

    2202624 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multisensor data fusion

    Varshney, P.K. | Tema Archive | 1997


    Multisensor data fusion

    Waltz, E. / Llinas, J. | Tema Archive | 1990


    Train localization using an adaptive multisensor data fusion technique

    Bidhan Malakar / Binoy Krishna Roy | DOAJ | 2019

    Free access

    Multisensor comparison and data fusion for mapping enclosed spaces

    Song, S. / Billoo, M. / Guest, C. et al. | British Library Online Contents | 2012


    Distributed multisensor data fusion

    NASBURG, R. / MORAVEC, K. | AIAA | 1984