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

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 (GM...

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Detalles Bibliográficos
Autores principales: Jadaliha, Mahdi, Jeong, Jinho, Xu, Yunfei, Choi, Jongeun, Kim, Junghoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164902/
https://www.ncbi.nlm.nih.gov/pubmed/30200257
http://dx.doi.org/10.3390/s18092866

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164902/
https://www.ncbi.nlm.nih.gov/pubmed/30200257
http://dx.doi.org/10.3390/s18092866

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