Dynamic Bayesian network for semantic place classification in mobile robotics


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 form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Publication DOI: https://doi.org/10.1007/s10514-016-9600-2
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © Springer Science+Business Media New York 2016. The final publication is available at Springer via http://dx.doi.org/10.1007%2Fs10514-016-9600-2
Uncontrolled Keywords: artificial intelligence,dynamic Bayesian network,semantic place recognition,Artificial Intelligence
Publication ISSN: 1573-7527
Last Modified: 18 Mar 2024 08:17
Date Deposited: 20 Dec 2016 16:00
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017
Published Online Date: 2016-07-28
Accepted Date: 2016-07-16
Authors: Premebida, Cristiano
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)
Nunes, Urbano



Version: Accepted Version

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