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Enhanced PCA-based localization using depth maps with missing data

2015-02Journal article Restricted access
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dc.contributor.authorCarreira, Fernando
dc.contributor.authorCalado, João Manuel Ferreira
dc.contributor.authorCardeira, Carlos
dc.contributor.authorOliveira, Paulo
dc.date.accessioned2016-03-08T15:49:30Z
dc.date.available2016-03-08T15:49:30Z
dc.date.issued2015-02
dc.description.abstractIn this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.pt_PT
dc.identifier.citationCARREIRA, Fernando; [et al.] - Enhanced PCA-based localization using depth maps with missing data. Journal of Intelligent & Robotics Systems. ISSN. 0921-0296. Vol. 77, N.º 2, SI (2015), pp. 341-360pt_PT
dc.identifier.doi10.1007/s10846-013-0013-6pt_PT
dc.identifier.issn0921-0296
dc.identifier.issn1573-0409
dc.identifier.urihttp://hdl.handle.net/10400.21/5809
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.ispartofseriesSI;
dc.relation.publisherversionhttp://link.springer.com/article/10.1007%2Fs10846-013-0013-6pt_PT
dc.subjectMobile robotspt_PT
dc.subjectRobot sensing systemspt_PT
dc.subjectSensor fusionpt_PT
dc.subjectPrincipal component analysispt_PT
dc.subjectKalman filterspt_PT
dc.titleEnhanced PCA-based localization using depth maps with missing datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage360pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage341pt_PT
oaire.citation.volume77pt_PT
person.familyNameNeves da Fonseca Cardoso Carreira
person.familyNameCalado
person.givenNameFernando Paulo
person.givenNameJoão
person.identifier370725
person.identifier.ciencia-idD019-D006-510B
person.identifier.ciencia-idB518-93E3-E7AB
person.identifier.orcid0000-0002-9873-5050
person.identifier.orcid0000-0001-6628-4657
person.identifier.ridM-4167-2013
person.identifier.scopus-author-id7006897277
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0cc1e99c-99cc-48dc-b13e-beacade8905d
relation.isAuthorOfPublication602b1546-f4f1-4cd5-8d29-d835d54c9bd6
relation.isAuthorOfPublication.latestForDiscovery602b1546-f4f1-4cd5-8d29-d835d54c9bd6

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