Simultaneous Localization And Mapping (SLAM) is considered as one of the key solutions for making mobile robots truly autonomous. Based mainly on perceptive information, the SLAM concept is assumed to solve localization and provide a map of the surrounding environment simultaneously. In this paper, we study SLAM limitations and we propose an approach to detect a priori potential failure scenarios for 2D laser-based SLAM methods. Our approach makes use of raw sensor data, which makes it independent of the underlying SLAM implementation, to extract a relevant descriptors vector. This descriptors vector is then used together with a decision-making algorithm to detect failure scenarios. Our approach is evaluated using different decision algorithms through three realistic experiments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Failure detection for laser-based SLAM in urban and peri-urban environments


    Contributors:


    Publication date :

    2017-10-01


    Size :

    650091 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SLAM for Flight Through Urban Environments Using Dimensionality Reduction

    Watkins, Adam / Kehoe, Joseph / Lind, Rick | AIAA | 2006


    Nonparametric Background Model-Based LiDAR SLAM in Highly Dynamic Urban Environments

    Park, Joohyun / Cho, Younggun / Shin, Young-Sik | IEEE | 2022


    AIAA-2006-6720 SLAM for Flight Through Urban Environments Using Dimensionality Reduction

    Watkins, A. / Kehoe, J. / Lind, R. et al. | British Library Conference Proceedings | 2006


    CABLE GUIDING DEVICE FOR URBAN OR PERI-URBAN AERIAL CABLEWAY SYSTEM

    GAVOTY SIMON / COUDURIER STEPHANE | European Patent Office | 2022

    Free access

    3D LiDAR SLAM Integration with GPS/INS for UAVs in Urban GPS-Degraded Environments

    Hening, Sebastian / Ippolito, Corey A. / Krishnakumar, Kalmanje S. et al. | AIAA | 2017