Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113285
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Type: Journal article
Title: Fully Bayesian field slam using Gaussian Markov random fields
Author: Do, H.N.
Jadaliha, M.
Temel, M.
Choi, J.
Citation: Asian Journal of Control, 2016; 18(4):1175-1188
Publisher: Wiley
Issue Date: 2016
ISSN: 1561-8625
1934-6093
Statement of
Responsibility: 
Huan N. Do, Mahdi Jadaliha, Mehmet Temel, and Jongeun Choi
Abstract: This paper presents a fully Bayesian way to solve the simultaneous localization and spatial prediction problem using a Gaussian Markov random field (GMRF) model. The objective is to simultaneously localize robotic sensors and predict a spatial field of interest using sequentially collected noisy observations by robotic sensors. The set of observations consists of the observed noisy positions of robotic sensing vehicles and noisy measurements of a spatial field. To be flexible, the spatial field of interest is modeled by a GMRF with uncertain hyperparameters. We derive an approximate Bayesian solution to the problem of computing the predictive inferences of the GMRF and the localization, taking into account observations, uncertain hyperparameters, measurement noise, kinematics of robotic sensors, and uncertain localization. The effectiveness of the proposed algorithm is illustrated by simulation results as well as by experiment results. The experiment results successfully show the flexibility and adaptability of our fully Bayesian approach in a data-driven fashion.
Keywords: Vision-based localization; spatial modelling; simultaneous localization and mapping (SLAM); Gaussian process regression; Gaussian Markov random field
Rights: © 2015 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd
RMID: 0030069425
DOI: 10.1002/asjc.1237
Appears in Collections:Computer Science publications

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