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dc.contributor.authorBauer, Zuria
dc.contributor.authorRangel, José Carlos
dc.contributor.authorCazorla, Miguel
dc.contributor.authorGomez Donoso, Francisco
dc.date.accessioned2020-01-06T14:31:20Z
dc.date.accessioned2020-01-06T14:31:20Z
dc.date.available2020-01-06T14:31:20Z
dc.date.available2020-01-06T14:31:20Z
dc.date.issued11/21/2018
dc.date.issued11/21/2018
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-99885-5_1
dc.identifier.urihttps://ridda2.utp.ac.pa/handle/123456789/9446
dc.identifier.urihttps://ridda2.utp.ac.pa/handle/123456789/9446
dc.descriptionIn social robotics, it is important that a mobile robot knows where it is because it provides a starting point for other activities such as moving from one room to another. As a contribution to solving this problem in the field of the semantic location of the mobile robot, we pro- pose to implement a methodology of recognition and scene learning in a real domestic environment. For this purpose, we used images from five different residences to create a dataset with which the base model was trained. The effectiveness of the implemented base model is evaluated in different scenarios. When the accuracy of the site identification decreases, the user provides feedback to the robot so that it can process the information collected from the new environment and re-identify the current location. The results obtained reinforce the need to acquire more knowledge when the environment is not recognizable by the pre-trained model.en_US
dc.description.abstractIn social robotics, it is important that a mobile robot knows where it is because it provides a starting point for other activities such as moving from one room to another. As a contribution to solving this problem in the field of the semantic location of the mobile robot, we pro- pose to implement a methodology of recognition and scene learning in a real domestic environment. For this purpose, we used images from five different residences to create a dataset with which the base model was trained. The effectiveness of the implemented base model is evaluated in different scenarios. When the accuracy of the site identification decreases, the user provides feedback to the robot so that it can process the information collected from the new environment and re-identify the current location. The results obtained reinforce the need to acquire more knowledge when the environment is not recognizable by the pre-trained model.en_US
dc.formatapplication/pdf
dc.languageeng
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectRoboticsen_US
dc.subjectDeep learningen_US
dc.subjectSemantic localizationen_US
dc.subjectCNN trainingen_US
dc.subjectNeural networksen_US
dc.subjectRobotics
dc.subjectDeep learning
dc.subjectSemantic localization
dc.subjectCNN training
dc.subjectNeural networks
dc.titleSemantic Localization of a Robot in a Real Homeen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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