Dynamic Bayesian network for semantic place classification in mobile robotics

Cristiano Premebida*, Diego R. Faria, Urbano Nunes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1161-1172
Number of pages12
JournalAutonomous Robots
Issue number5
Early online date28 Jul 2016
Publication statusPublished - 2017

Bibliographical note

© 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


  • artificial intelligence
  • dynamic Bayesian network
  • semantic place recognition


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