  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.
