\abstract{
Within this work we present an updated version of our award-winning indoor localization system for smartphones. 
The current position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. 
Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access-points and signal strength predictions.
Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access-points, we use an optimization scheme based on a few reference measurements to estimate a corresponding \docWIFI{} model.
To model the pedestrian's movement, which is constrained by walls and other obstacles, we propose a state transition based upon navigation meshes, modeling only the building's walkable areas.
Continuous and smooth floor changes are enabled by using a simple activity recognition. 
Our rapid computation scheme of the kernel density estimation allows to find an exact estimation of the pedestrian's current position. 
We further tackle advanced problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures, leading to a more robust localization. 
\newline
The goal of this work is to propose a fast to deploy and low-cost localization solution, that
provides reasonable results in a high variety of situations.
To stress our system, we have chosen a very challenging test scenario. 
All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. 
The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{310}{\meter} length and \SI{10}{\minute} duration.
It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. 
Our advanced filtering methods allow for a real fail-safe system, while the novel optimization scheme enables a setup-time of under \SI{120}{\minute} for the complete building.
%We are able to resolve sample impoverishment whenever it occurs and thus provide a real fail-safe system. 
%finally compare the standard weighted-average estimator with our kernel density approach. 
}
