\abstract{
Within this work we present an updated version of our \del{award-winning} indoor localization system for smartphones. 
The \add{pedestrian's}  position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. 
Our \del{rapid computation} \add{recently presented approximation} scheme of the kernel density estimation allows to find an exact estimation of the current position\add{, instead of classical methods like weighted-average}. 
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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.
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\add{This work provides three major contributions to the system.}
\add{The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building's walkable areas.}
\add{The localization system is further updated by replacing the previous activity recognition with a threshold-based algorithm using barometer and accelerometer readings, allowing for continuous and smooth floor changes.}
\add{Within the scope of this work,} we tackle \del{advanced} problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures \del{, leading to a more robust localization}. 
\add{For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter.}
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%TODO: additional contributions in den experimenten.
\newline
The goal of this work is to propose a fast to deploy \del{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. 
\del{Our advanced} \add{The introduced} filtering methods allow for a real fail-safe system, while the optimization scheme enables a setup-time of under \SI{120}{\minute} for the \del{complete building}  \add{building's \SI{2500}{\square\meter} walkable area}. 
%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. 
}
