Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/93185
DC Field | Value | Language |
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dc.contributor.author | Premebida, Cristiano | - |
dc.contributor.author | Faria, Diego R. | - |
dc.contributor.author | Nunes, Urbano | - |
dc.date.accessioned | 2021-02-23T17:07:00Z | - |
dc.date.available | 2021-02-23T17:07:00Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 0929-5593 | pt |
dc.identifier.issn | 1573-7527 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/93185 | - |
dc.description.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. | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer Nature | pt |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147323/PT | pt |
dc.relation | info:eu-repo/grantAgreement/FCT/5876-PPCDTI/126287/PT | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ | pt |
dc.subject | Semantic place recognition | pt |
dc.subject | Dynamic Bayesian network | pt |
dc.subject | Artificial intelligence | pt |
dc.title | Dynamic Bayesian network for semantic place classification in mobile robotics | pt |
dc.type | article | - |
degois.publication.firstPage | 1161 | pt |
degois.publication.lastPage | 1172 | pt |
degois.publication.issue | 5 | pt |
degois.publication.title | Autonomous Robots | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1007/s10514-016-9600-2 | pt |
degois.publication.volume | 41 | pt |
dc.date.embargo | 2016-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.openairetype | article | - |
item.languageiso639-1 | en | - |
item.fulltext | Com Texto completo | - |
item.cerifentitytype | Publications | - |
crisitem.author.researchunit | ISR - Institute of Systems and Robotics | - |
crisitem.author.researchunit | ISR - Institute of Systems and Robotics | - |
crisitem.author.parentresearchunit | University of Coimbra | - |
crisitem.author.parentresearchunit | University of Coimbra | - |
crisitem.author.orcid | 0000-0002-2168-2077 | - |
crisitem.author.orcid | 0000-0002-7750-5221 | - |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Premebida2017_Article_DynamicBayesianNetworkForSeman.pdf | 1.87 MB | Adobe PDF | View/Open |
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