Localisation via the fusion of spatially referring natural language statements is considered here. The contribution lies in the underlying problem formulation and a robust modelling framework. Random-set-based estimation is the underlying mathematical formalism. Each statement generates a generalised likelihood function. A Bayesian filter is outlined that takes a sequence of likelihoods generated by multiple statements. The idea is to recursively build a map over the state space that can be used to infer the state.


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    Title :

    Fusion of Spatially Referring Natural Language Statements with Random Set Theoretic Likelihoods


    Contributors:
    Bishop, A. N. (author) / Ristic, B. (author)


    Publication date :

    2013-04-01


    Size :

    6302191 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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