publication . Article . 2016

Dynamic Bayesian network for semantic place classification in mobile robotics

Cristiano Premebida; Diego R. Faria; Urbano Nunes;
Open Access English
  • Published: 28 Jul 2016
  • Country: United Kingdom
In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the fo...
free text keywords: Artificial Intelligence, Additive smoothing, Robot, Dynamic Bayesian network, Mixture model, Data mining, computer.software_genre, computer, Prior probability, Robotics, Bayesian probability, Machine learning, Computer science, business.industry, business, Probabilistic logic
Related Organizations
Funded by
FCT| UID/EEA/00048/2013
  • Funder: Fundação para a Ciência e a Tecnologia, I.P. (FCT)
  • Project Code: 147323
  • Funding stream: 5876
FCT| RECI/EEI-AUT/0181/2012
  • Funder: Fundação para a Ciência e a Tecnologia, I.P. (FCT)
  • Project Code: 126287
  • Funding stream: COMPETE
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