用CSI数据和混合神经网络实现机器人高精度室内定位
Hybrid Neural Network-Based Indoor Localisation System for Mobile Robots Using CSI Data in a Robotics Simulator
- CNN+MLP混合模型处理CSI转图像数据
- 在仿真环境中实现厘米级定位精度
- 适配ROS与卡尔曼滤波，适合智能机器人开发
我们提出一种基于大规模MIMO系统信道状态信息（CSI）数据的混合神经网络模型，用于推断移动机器人的位置。通过现有CSI数据集，将CSI读数利用TINTO工具转换为合成图像，并结合卷积神经网络（CNN）与多层感知机（MLP）构建混合神经网络（HyNN），实现2D位置估计。该定位方案集成于机器人仿真器及机器人操作系统（ROS），支持多样测试场景评估，并可集成卡尔曼滤波等状态估计算法。实验表明，该方法在复杂环境中具备高精度定位与导航潜力。研究还提出了一套可迁移的通用流程，适用于不同场景与数据集。
原文摘要 · Abstract (English)
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.
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