Gaussian Process Models for Indoor and Outdoor Sensor-Centric Robot Localization

Brooks, Alex, Makarenko, Alexei and Upcroft, Ben (2008) Gaussian Process Models for Indoor and Outdoor Sensor-Centric Robot Localization. IEEE Transactions on Robotics, 24 6: 1341-1351. doi:10.1109/TRO.2008.2004887


Author Brooks, Alex
Makarenko, Alexei
Upcroft, Ben
Title Gaussian Process Models for Indoor and Outdoor Sensor-Centric Robot Localization
Journal name IEEE Transactions on Robotics   Check publisher's open access policy
ISSN 1552-3098
Publication date 2008-12-01
Year available 2008
Sub-type Article (original research)
DOI 10.1109/TRO.2008.2004887
Open Access Status DOI
Volume 24
Issue 6
Start page 1341
End page 1351
Total pages 11
Editor R. A. Volz
Place of publication United States
Publisher IEEE
Language eng
Subject C1
09 Engineering
0913 Mechanical Engineering
8899 Other Transport
Abstract This paper presents an approach to building a map from a sparse set of noisy 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 (GP) framework. The approach is validated experimentally both in an indoor office environment and an outdoor urban environment, using observations from an omnidirectional camera mounted on a mobile robot. A set of training data is collected from each environment and processed offline to produce a GP model. The robot then uses that model to localize while traversing each environment.
Keyword Appearance-based localization
Gaussian processes
mobile robot localization
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
School of Mechanical & Mining Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 18 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 21 times in Scopus Article | Citations
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Created: Thu, 09 Apr 2009, 22:00:32 EST by Gail Smith on behalf of School of Mechanical and Mining Engineering