The simultaneous localization and mapping (SLAM) problem involves using the measurements of sensors to construct an environmental map, while simultaneously recovering the vehicle trajectory within this map. There are broadly two strategies for SLAM: on-line and off-line. In this paper, we focus on the off-line SLAM (a.k.a. full SLAM) problem and propose a variational Bayes inference algorithm to address it. Specifically, the intractable posterior distribution of the vehicle poses given the measurements is approximated by a tractable variational distribution, resulting in estimates of the vehicle poses as well as their uncertainties. In contrast with the existing off- line methods, the inverse variances of the additive noise are updated along with the posterior distribution instead of being fixed, thus, the proposed method is robust to unknown noises. Furthermore, the computational complexity of the proposed method is only linear in the number of frames and the computational bottleneck of the algorithm can be easily parallelized to achieve further acceleration. Numerical results show that the proposed method is insensitive to the selection of the noise parameters. More importantly, it is superior in efficiency to the state-of-the-art method, especially for large- scale SLAM problems.


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    Titel :

    Robust Linear-Complexity Approach to Full SLAM Problems: Stochastic Variational Bayes Inference


    Beteiligte:
    Jiang, Xiaoyue (Autor:in) / Yu, Hang (Autor:in) / Hoy, Michael (Autor:in) / Dauwels, Justin (Autor:in)


    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    447742 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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