Towards large-scale occupancy map building using Dirichlet and Gaussian processes

Soohwan Kim*, Jonghyuk Kim

*Corresponding author for this work

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

    7 Citations (Scopus)

    Abstract

    This paper proposes a new method for building occupancy maps using Dirichlet and Gaussian processes. We consider occupancy map building as a classification problem and apply Gaussian processes. The main drawback of Gaussian processes, however, is the computational complexity of O(n 3) related to the matrix inversion, where n is the number of data points. To enable large-scale occupancy map building, we propose to use Dirichlet process mixture models which cluster input data without fixing the number of clusters a priori and to apply a mixture of Gaussian processes for the clustered data. This approach also has an advantage of dealing with local discontinuities better than one global Gaussian process model. Simulation results will be provided demonstrating the benefits of the approach.

    Original languageEnglish
    Title of host publicationProceedings of the 2011 Australasian Conference on Robotics and Automation
    Publication statusPublished - 2011
    Event2011 Australasian Conference on Robotics and Automation - Melbourne, VIC, Australia
    Duration: 7 Dec 20119 Dec 2011

    Publication series

    NameProceedings of the 2011 Australasian Conference on Robotics and Automation

    Conference

    Conference2011 Australasian Conference on Robotics and Automation
    Country/TerritoryAustralia
    CityMelbourne, VIC
    Period7/12/119/12/11

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