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Open AccessArticle

Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields

1
Monsanto, St. Louis, MO 63146, USA
2
School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea
3
Denso International America, Inc., San Jose, CA 95110, USA
4
Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2866; https://doi.org/10.3390/s18092866
Received: 3 July 2018 / Revised: 13 August 2018 / Accepted: 26 August 2018 / Published: 30 August 2018
(This article belongs to the Special Issue Deep Learning Based Sensing Technologies for Autonomous Vehicles)
In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GMRF) with unknown hyperparameters. We first derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account observations, uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution for uncertain localization is not scalable as the number of observations increases. To cope with this exponentially increasing complexity and to be usable for mobile sensor networks with limited resources, we propose a scalable approximation with a controllable trade-off between approximation error and complexity to the exact solution. The effectiveness of the proposed algorithms is demonstrated by simulation and experimental results. View Full-Text
Keywords: Gaussian markov random field; fully Bayesian; mobile sensor network; localization uncertainty Gaussian markov random field; fully Bayesian; mobile sensor network; localization uncertainty
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Jadaliha, M.; Jeong, J.; Xu, Y.; Choi, J.; Kim, J. Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields. Sensors 2018, 18, 2866.

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