Abstract One of the most challenging aspects of concurrent mapping and localization (CML) is the problem of data association. Because of uncertainty in the origins of sensor measurements, it is difficult to determine the correspondence between measured data and features of the scene or object being observed, while rejecting spurious measurements. This paper reviews several new approaches to data association and feature modeling for CML that share the common theme of combining information from multiple uncertain vantage points while rejecting spurious data. Our results include: (1) feature-based mapping from laser data using robust segmentation, (2) map-building with sonar data using a novel application of the Hough transform for perception grouping, and (3) a new stochastic framework for making delayed decisions for combination of data from multiple uncertain vantage points. Experimental results are shown for CML using laser and sonar data from a B21 mobile robot.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Towards Robust Data Association and Feature Modeling for Concurrent Mapping and Localization


    Contributors:


    Publication date :

    2003-01-01


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Feature-based concurrent mapping and localization for AUVs

    Smith, C.M. / Leonard, J.J. / Bennett, A.A. et al. | Tema Archive | 1997


    Cooperative concurrent mapping and localization

    Fenwick, J.W. / Newman, P.M. / Leonard, J.J. | Tema Archive | 2002


    Experimental analysis of adaptive concurrent mapping and localization using sonar

    Leonard, John J. / Feder, Hans Jacob S. | Springer Verlag | 2008


    A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

    Thrun, S. / Burgard, W. / Fox, D. | British Library Online Contents | 1998