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    A PROBABILISTIC MULTIMODAL ALGORITHM FOR TRACKING MULTIPLE AND DYNAMIC OBJECTS

    TitleA PROBABILISTIC MULTIMODAL ALGORITHM FOR TRACKING MULTIPLE AND DYNAMIC OBJECTS
    Publication TypeBook Chapter
    Año de publicación2004
    AutoresMarron, M, Sotelo, MA, Garcia, JC
    Book TitleROBOTICS: TRENDS, PRINCIPLES AND APPLICATIONS
    Series TitleIntelligent Automation and Soft Computing
    Volumen15
    Páginas511-516
    Fecha de publicación07/2004
    EditorialTSI Press
    CityAlburquerque (USA)
    Idioma de publicaciónEnglish
    Numero ISBN1-889335-21-5
    Palabras claveCrowded Environments, Multi-Object Tracking, Particle Filters, Probabilistic Algorithms
    Abstract

    The work presented is related to the research area of autonomous navigation for mobile robots in unstructured, heavily crowded, and highly dynamic environments. One of the main tasks involved in this research topic is the obstacle tracking module that has been successfully developed with different kind of probabilistic algorithms. The reliability that these techniques have shown estimating position with noisy measurements make them the most adequate to the mentioned problem, but their high computational cost has made them only useful with few objects. In this paper a computational simple solution based on a multimodal particle filter is proposed to track multiple and dynamic obstacles in an unstructured environment and based on the noisy position measurements taken from sonar sensors.

    Resumen

    The work presented is related to the research area of autonomous navigation for mobile robots in unstructured, heavily crowded, and highly dynamic environments. One of the main tasks involved in this research topic is the obstacle tracking module that has been successfully developed with different kind of probabilistic algorithms. The reliability that these techniques have shown estimating position with noisy measurements make them the most adequate to the mentioned problem, but their high computational cost has made them only useful with few objects. In this paper a computational simple solution based on a multimodal particle filter is proposed to track multiple and dynamic obstacles in an unstructured environment and based on the noisy position measurements taken from sonar sensors.

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