Ballesteros Jérez, JavierMartínez Gómez, JesúsGarcía Varea, IsmaelOrozco Barbosa, LuisCastillo-Cara, Manuel2025-12-042025-12-042025-10-01Ballesteros-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 Switzerland9783032009852https://doi.org/10.1007/978-3-032-00986-9_11https://hdl.handle.net/20.500.14468/31016The 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_11La 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_11We 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.eninfo:eu-repo/semantics/openAccess1203.04 Inteligencia artificialactas de congresoIndoor localisationPositioningDeep LearningHybrid Neural NetworkRobotics Simulation