Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/31595
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCéspedes, Orlando Jose-
dc.contributor.authorCebollada, Sergio-
dc.contributor.authorCabrera, Juan José-
dc.contributor.authorReinoso, Oscar-
dc.contributor.authorPaya, Luis-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Sistemas y Automáticaes_ES
dc.date.accessioned2024-02-28T12:18:01Z-
dc.date.available2024-02-28T12:18:01Z-
dc.date.created2023-06-
dc.identifier.urihttps://hdl.handle.net/11000/31595-
dc.description.abstractThis work presents an evaluation regarding the use of data augmentation to carry out the rough localization step within a hierarchical localization framework. The method consists of two steps: first, the robot captures an image and it is introduced into a CNN in order to estimate the room where it was captured (rough localization). After that, a holistic descriptor is obtained from the network and it is compared with the descriptors stored in the model. The most similar image provides the position where the robot captured the image (fine localization). Regarding the rough localization, it is essential that the CNN achieves a high accuracy, since an error in this step would imply a considerable localization error. With this aim, several visual effects were separately analyzed in order to know their impact on the CNN when data augmentation is tackled. The results permit designing a data augmentation which is useful for training a CNN that solves the localization problem in real operation conditions, including changes in the lighting conditionses_ES
dc.formatapplication/pdfes_ES
dc.format.extent12es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofArtificial Intelligence Applications and Innovations 19th IFIP WG 12.5 International Conference, AIAI 2023, León, Spain, June 14–17, 2023, Proceedings, Part Ies_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMobile Roboticses_ES
dc.subjectOmnidirectional Visiones_ES
dc.subjectHierarchical Localizationes_ES
dc.subjectDeep Learninges_ES
dc.subjectData Augmentationes_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleAnalysis of Data Augmentation Techniques for Mobile Robots Localization by Means of Convolutional Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:
Artículos Ingeniería de Sistemas y Automática


no-thumbnailView/Open:

 11-978-3-031-34111-3_42 (1).pdf



736,12 kB
Adobe PDF
Share:


Creative Commons ???jsp.display-item.text9???