We 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.
翻译：我们提出了一种混合神经网络模型，用于利用大规模MIMO系统的信道状态信息（CSI）数据推断移动机器人的位置。通过利用现有的CSI数据集，我们的方法将卷积神经网络（CNN）与多层感知器（MLP）相结合，构建了一个混合神经网络（HyNN）来估计机器人的二维位置。CSI读数通过TINTO工具转换为合成图像。该定位解决方案与机器人仿真器及机器人操作系统（ROS）集成，便于通过异构测试用例进行评估，并支持采用如卡尔曼滤波器等状态估计器。我们的贡献展示了HyNN模型在复杂环境中实现移动机器人精确室内定位与导航的潜力。本研究遵循并提出了一个可推广的流程，适用于所研究的具体用例之外，使其能够适应不同场景和数据集。
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