Fully Bayesian simultaneous localization and spatial prediction using Gaussian Markov random fields (GMRFs)

Mahdi Jadaliha, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper investigates a fully Bayesian way to solve the simultaneous localization and spatial prediction (SLAP) 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 obtained noisy observations collected by robotic sensors. The set of observations consists of the observed uncertain poses 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.

Original languageEnglish
Title of host publication2013 American Control Conference, ACC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4592-4597
Number of pages6
ISBN (Print)9781479901777
DOIs
Publication statusPublished - 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: 2013 Jun 172013 Jun 19

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period13/6/1713/6/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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