QUT ePrints

Gaussian process models for sensor-centric robot localisation

Brooks, Alex, Makarenko, Alexei, & Upcroft, Ben (2006) Gaussian process models for sensor-centric robot localisation. In Papanikolopoulos, N (Ed.) Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., IEEE, Orlando, Florida, pp. 56-61.

[img] Published Version (PDF 780kB)
Administrators only | Request a copy from author

    View at publisher

    Abstract

    This paper presents an approach to building an observation likelihood function from a set of sparse, noisy training observations taken from known locations by a sensor with no obvious geometric model. The basic approach is to fit an interpolant to the training data, representing the expected observation, and to assume additive sensor noise. This paper takes a Bayesian view of the problem, maintaining a posterior over interpolants rather than simply the maximum-likelihood interpolant, giving a measure of uncertainty in the map at any point. This is done using a Gaussian process framework. To validate the approach experimentally, a model of an environment is built using observations from an omni-directional camera. After a model has been built from the training data, a particle filter is used to localise while traversing this environment

    Impact and interest:

    4 citations in Scopus
    Search Google Scholar™
    2 citations in Web of Science®

    Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

    These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

    Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

    ID Code: 48370
    Item Type: Conference Paper
    DOI: 10.1109/ROBOT.2006.1641161
    ISBN: 0-7803-9505-0
    ISSN: 1050-4729
    Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
    Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
    Copyright Owner: ©2006 IEEE
    Deposited On: 31 Jan 2012 15:34
    Last Modified: 11 Mar 2012 15:48

    Export: EndNote | Dublin Core | BibTeX

    Repository Staff Only: item control page