Mostrar el registro sencillo del ítem

dc.contributor.authorCruz, Edmanuel
dc.contributor.authorRangel, José Carlos
dc.contributor.authorCazorla, Miguel
dc.date.accessioned2020-01-02T21:09:02Z
dc.date.accessioned2020-01-02T21:09:02Z
dc.date.available2020-01-02T21:09:02Z
dc.date.available2020-01-02T21:09:02Z
dc.date.issued11/12/2017
dc.date.issued11/12/2017
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-70833-1_46
dc.identifier.isbn978-3-319-70833-1
dc.identifier.urihttps://ridda2.utp.ac.pa/handle/123456789/9444
dc.identifier.urihttps://ridda2.utp.ac.pa/handle/123456789/9444
dc.descriptionSemantic localization for mobile robots involves an accurate determination of the kind of place where a robot is located. Therefore, the representation of the knowledge of this place is crucial for the robot. In this paper we present a study for finding a robust model for scene classification procedure for a mobile robot. The proposed system uses CNN descriptors for representing the input perceptions of the robot. First, we develop comparative experiments in order for finding a model. Experiments include the evaluation of several pre-trained CNN models and training a classifier with different classifications algorithms. These experiments were carried out using the ViDRILO dataset and compared with the benchmark provided by their authors. The results demonstrate the goodness of using CNN descriptors for semantic classification.en_US
dc.description.abstractSemantic localization for mobile robots involves an accurate determination of the kind of place where a robot is located. Therefore, the representation of the knowledge of this place is crucial for the robot. In this paper we present a study for finding a robust model for scene classification procedure for a mobile robot. The proposed system uses CNN descriptors for representing the input perceptions of the robot. First, we develop comparative experiments in order for finding a model. Experiments include the evaluation of several pre-trained CNN models and training a classifier with different classifications algorithms. These experiments were carried out using the ViDRILO dataset and compared with the benchmark provided by their authors. The results demonstrate the goodness of using CNN descriptors for semantic classification.en_US
dc.formatapplication/pdf
dc.languageeng
dc.language.isoenen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectSemantic localizationen_US
dc.subjectDeep Learningen_US
dc.subjectEnvironment perceptionen_US
dc.subjectImage processingen_US
dc.subjectSemantic localization
dc.subjectDeep Learning
dc.subjectEnvironment perception
dc.subjectImage processing
dc.titleRobot Semantic Localization Through CNN Descriptorsen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem