Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface

TL;DR

This paper introduces a novel approach combining radio sensing and deep learning to reconstruct indoor floor plans from radio environmental maps obtained via a Large Intelligent Surface, enhancing autonomous navigation and communication reliability.

Contribution

It proposes a new method using LS, U-Net, and cGANs to accurately convert radio environmental maps into detailed indoor floor plans, integrating radio sensing with deep learning.

Findings

Floor plans can be accurately reconstructed from radio maps.

Both local and global measurements are effective.

Deep learning models improve reconstruction quality.

Abstract

Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interaction with robots. Equally important, it is also vital to ensure reliable communication between the robot and its controller. Large Intelligent Surface (LIS) is a technology that has been extensively studied due to its communication capabilities. Moreover, due to the number of antenna elements, these surfaces arise as a powerful solution to radio sensing. This paper presents a novel method to translate radio environmental maps obtained at the LIS to floor plans of the indoor environment built of scatterers spread along its area. The usage of a Least Squares (LS) based method, U-Net (UN) and conditional Generative Adversarial Networks (cGANs) were leveraged to perform this task. We show that the floor plan can be correctly reconstructed using both local and global measurements.