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

Mauricio Soto-Alvarez, Petri Honkamaa

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

    5 Citations (Scopus)

    Abstract

    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 publicationIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages6543-6547
    ISBN (Electronic)978-1-4799-2893-4
    ISBN (Print)978-1-4799-2892-7
    DOIs
    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

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    Abbreviated titleICASSP 2014
    Country/TerritoryItaly
    CityFlorence
    Period4/05/149/05/14

    Keywords

    • algorithms
    • localisation
    • mapping
    • data association

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