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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Jiang, Xiaoyue (author) / Yu, Hang (author) / Hoy, Michael (author) / Dauwels, Justin (author)


    Publication date :

    2019-09-01


    Size :

    447742 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Linear-complexity stochastic variational Bayes inference for SLAM

    Jiang, Xiaoyue / Hoy, Michael / Yu, Hang et al. | IEEE | 2017


    Normalized Gaussian Network Based on Variational Bayes Inference and Hierarchical Model Selection

    Yoshimoto, J. / Ishii, S. / Sato, M.-a. | British Library Online Contents | 2003


    Variational-Bayes Optical Flow

    Chantas, G. | British Library Online Contents | 2014


    RWT-SLAM: Robust Visual SLAM for Weakly Textured Environments

    Peng, Qihao / Zhao, Xijun / Dang, Ruina et al. | IEEE | 2024


    Trajectory Prediction Algorithm Based on Variational Bayes

    Ma, Xiaolong / Liu, Gang / He, Bing et al. | IEEE | 2018