
\begin{abstract}
DUMMY ABSTRACT. We present an indoor localisation system that integrates different sensor modalities,
namely Wi-Fi, barometer, iBeacons, step- and turn-detection for localisation of pedestrians within buildings
over multiple floors. To model the pedestrian's movement, which is constrained by walls and other obstacles,
we propose a state transition based upon random walks on graphs. \sout{This model also frees us from the burden of
frequently updating the system.}
In addition we make use of barometer information to estimate the current floor.
\commentByFrank{entweder alle sensoren nennen, oder weglassen? sonst wirkt es nicht schluessig}ds
Furthermore, we present a statistical approach to avoid the incorporation of faulty heading information caused
by changing the smartphone's position. 
\commentByFrank{ueber statistical reden wir nochma. einerseits ja, andererseits irgendwie nein.}
The evaluation of the system within a $\SI{77}{\meter}$ $\times$ $\SI{55}{\meter}$ sized building with 4 floors
shows that high accuracy can be achieved while also keeping the update-rates low. 
\commentByFrank{We will show that incorporating prior knowledge, such as the pedestrian's desired destination,
improves the overall localisation process and prevents various error-conditions.}

\commentByToni{Das ist der alte Abstract vom letzten Paper. :D Da wollte ich noch nen ganz neuen schreiben. Das mach ich aber immer gaaaanz am ende}
\end{abstract}
