Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/93185
Title: Dynamic Bayesian network for semantic place classification in mobile robotics
Authors: Premebida, Cristiano 
Faria, Diego R.
Nunes, Urbano 
Keywords: Semantic place recognition; Dynamic Bayesian network; Artificial intelligence
Issue Date: 2016
Publisher: Springer Nature
Project: info:eu-repo/grantAgreement/FCT/5876/147323/PT 
info:eu-repo/grantAgreement/FCT/5876-PPCDTI/126287/PT 
Serial title, monograph or event: Autonomous Robots
Volume: 41
Issue: 5
Abstract: 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.
URI: http://hdl.handle.net/10316/93185
ISSN: 0929-5593
1573-7527
DOI: 10.1007/s10514-016-9600-2
Rights: openAccess
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais

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