  Personal indoor localization is usually accomplished by fusing information
from various sensors. A common choice is to use the WiFi adapter that provides
information about Access Points that can be found in the vicinity.
Unfortunately, state-of-the-art approaches to WiFi-based localization often
employ very dense maps of the WiFi signal distribution, and require a
time-consuming process of parameter selection. On the other hand, camera images
are commonly used for visual place recognition, detecting whenever the user
observes a scene similar to the one already recorded in a database. Visual
place recognition algorithms can work with sparse databases of recorded scenes
and are in general simple to parametrize. Therefore, we propose a WiFi-based
global localization method employing the structure of the well-known FAB-MAP
visual place recognition algorithm. Similarly to FAB-MAP our method uses
Chow-Liu trees to estimate a joint probability distribution of re-observation
of a place given a set of features extracted at places visited so far. However,
we are the first who apply this idea to recorded WiFi scans instead of visual
words. The new method is evaluated on the UJIIndoorLoc dataset used in the
EvAAL competition, allowing fair comparison with other solutions.
