Topological Indoor Mapping through WiFi Signals
This work addresses indoor localization and mapping for users in short-lived events like conferences, though it appears incremental as it builds on existing WiFi-based approaches.
The paper tackles the problem of indoor mapping using WiFi signals by developing an unsupervised method that creates topological maps from signal strength distributions, dimension reduction, and clustering, achieving results without requiring additional infrastructure.
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.