Topological Indoor Mapping through WiFi Signals

TL;DR

This paper presents an unsupervised WiFi-based topological indoor mapping method that constructs discrete location maps without additional infrastructure, suitable for dynamic environments and short-term events.

Contribution

It introduces a novel unsupervised approach using WiFi signal distributions, dimension reduction, and clustering for topological indoor mapping without extra infrastructure.

Findings

Effective in dynamic environments with changing conditions

Suitable for short-lived indoor events like conferences

Does not require additional infrastructure or manual map-building

Abstract

The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.