Multiple hypotheses data association propagation for robust monocular-based SLAM algorithms

M. Soto-Alvarez, Petri Honkamaa

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    4 Citations (Scopus)


    Data Association is probably the most important step of every monocular Simultaneous Localization and Mapping (SLAM) algorithm because it provides the basic information to the estimation module, independently on the estimation algorithm of choice. Although important, it is also a difficult task because the analytic solution is NP-Hard. The usual approximation is obtaining only one data association hypothesis per frame which affects the robustness of the result [1][2][3][4][5]. In this paper, a data association approach is presented, where multiple hypotheses are propagated between frames using a probabilistic framework. Experimental results, using real and synthetic data, show that the proposed algorithm produces promising results with respect to other state of the art methods.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4799-2893-4
    ISBN (Print)978-147992892-7
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
    Duration: 4 May 20149 May 2014


    ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    Abbreviated titleICASSP 2014


    • algorithms
    • localisation
    • mapping
    • data association

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