Fully Bayesian Field Slam Using Gaussian Markov Random Fields

Huan N. Do, Mahdi Jadaliha, Mehmet Temel, Jongeun Choi

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1175-1188
Number of pages14
JournalAsian Journal of Control
Issue number4
Publication statusPublished - 2016 Jul 1

Bibliographical note

Publisher Copyright:
© 2015 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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