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Hybrid Neural Network-Based Indoor Localisation System for Mobile Robots Using CSI Data in a Robotics Simulator

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dc.contributor.authorBallesteros Jérez, Javier
dc.contributor.authorMartínez Gómez, Jesús
dc.contributor.authorGarcía Varea, Ismael
dc.contributor.authorOrozco Barbosa, Luis
dc.contributor.authorCastillo-Cara, Manuel
dc.coverage.spatialOporto
dc.coverage.temporal2025-02-25
dc.date.accessioned2025-12-04T12:43:58Z
dc.date.available2025-12-04T12:43:58Z
dc.date.issued2025-10-01
dc.descriptionThe registered version of this conference paper, first published in "Robotics, Computer Vision and Intelligent Systems - 5th International Conference, ROBOVIS 2025, Proceedings", is available online at the publisher's website: https://doi.org/10.1007/978-3-032-00986-9_11
dc.descriptionLa versión registrada de esta comunicación, publicada por primera vez en "Robotics, Computer Vision and Intelligent Systems - 5th International Conference, ROBOVIS 2025, Proceedings", está disponible en línea en el sitio web del editor: https://doi.org/10.1007/978-3-032-00986-9_11
dc.description.abstractWe present a hybrid neural network model for inferring the position of mobile robots using Channel State Information (CSI) data from a Massive MIMO system. By leveraging an existing CSI dataset, our approach integrates a Convolutional Neural Network (CNN) with a Multilayer Perceptron (MLP) to form a Hybrid Neural Network (HyNN) that estimates 2D robot positions. CSI readings are converted into synthetic images using the TINTO tool. The localisation solution is integrated with a robotics simulator, and the Robot Operating System (ROS), which facilitates its evaluation through heterogeneous test cases, and the adoption of state estimators like Kalman filters. Our contributions illustrate the potential of our HyNN model in achieving precise indoor localisation and navigation for mobile robots in complex environments. The study follows, and proposes, a generalisable procedure applicable beyond the specific use case studied, making it adaptable to different scenarios and datasets.en
dc.description.versionversión final
dc.identifier.citationBallesteros-Jerez, J., Martínez-Gómez, J., García-Varea, I., Orozco-Barbosa, L., & Castillo-Cara, M. (2025, February). Hybrid Neural Network-Based Indoor Localisation System for Mobile Robots Using CSI Data in a Robotics Simulator. In International Conference on Robotics, Computer Vision and Intelligent Systems (pp. 148-163). Cham: Springer Nature Switzerland
dc.identifier.doihttps://doi.org/10.1007/978-3-032-00986-9_11
dc.identifier.isbn9783032009852
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31016
dc.language.isoen
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.congress5th International Conference on Robotics, Computer Vision and Intelligent Systems, Porto, Portugal, February 25–27, 2025
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203.04 Inteligencia artificial
dc.subject.keywordsIndoor localisationen
dc.subject.keywordsPositioningen
dc.subject.keywordsDeep Learningen
dc.subject.keywordsHybrid Neural Networken
dc.subject.keywordsRobotics Simulationen
dc.titleHybrid Neural Network-Based Indoor Localisation System for Mobile Robots Using CSI Data in a Robotics Simulatoren
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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