Research Project 2: Drone-supported AI-based Generation of 3D Maps of Indoor Radio Environments

TL;DR

This paper presents an autonomous drone-based system for efficiently generating 3D Radio Environment Maps of indoor spaces, utilizing machine learning to predict signal strength in unvisited locations.

Contribution

It introduces a scalable method combining multiple drones and machine learning to automate and improve 3D radio environment mapping indoors.

Findings

Drones visited 36 waypoints each, collecting thousands of WiFi samples.

Collected data was analyzed to understand indoor WiFi signal distribution.

Machine learning techniques can predict WiFi signal strength in unvisited locations.

Abstract

A Radio Environment Map (REM) is a powerful tool in enhancing the experience of radio-enabled agents but building such a REM can be a laborious undertaking, especially in three dimensions. This project shows how such a REM of an indoor three-dimensional space can be generated in an autonomous and scalable way. Building on the results of the preceding Research Project 1, multiple drones are used to map the WiFi signals present in such a space in a real-world environment where the drones are each able to visit 36 waypoints and collectively gather thousands of WiFi beacon data samples. This report also includes an analysis of the collected data and concludes by proposing machine-learning based techniques to predict the signal strength of known access points in locations not visited by the drones.