The general simultaneous localization and mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with map building of the local environment. Current state-of-the-art methods such as given by Williams et al. relies on nonlinear least-squares (NLS) batch formulations with structure exploitation for memory efficiency and speed. We investigate the expectation-maximization (EM) algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively yielding a low computational complexity. The iterations switch between state estimation, which can use any state-space smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. The results show that EM-SLAM has much lower computational complexity than NLS while maintaining comparable accuracy.


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    Titel :

    EM-SLAM With Inertial/Visual Applications


    Beteiligte:


    Erscheinungsdatum :

    2017-02-01


    Format / Umfang :

    982429 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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