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

TL;DR

This paper introduces a hybrid neural network model combining CNN and MLP to accurately localize mobile robots indoors using CSI data, integrated within a robotics simulator for evaluation.

Contribution

The paper presents a novel hybrid neural network approach for indoor robot localization using CSI data, with a generalizable procedure adaptable to various scenarios.

Findings

Achieved precise 2D localization of robots in complex environments.

Successfully integrated CSI-based localization with robotics simulation and ROS.

Demonstrated the model's adaptability to different datasets and scenarios.

Abstract

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.