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 \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. 
-%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}
+Navigating to a desired destination is a key aspect of indoor localisation. Up to this point many different systems using present or past information for estimating the pedestrian's position were presented. 
+Our work proposes a novel approach that incorporates prior navigation knowledge by using realistic human walking paths.
+In order to create such paths, we present a method that assigns an importance factor to every node of a regular tessellated graph by avoiding walls and detecting doors. 
+The human movement is then modelled by moving along adjacent nodes into the most proper walking-direction. 
+To be able of going into the 3rd dimension, realistically shaped stairs for step-wise floor changes are used. 
+The position is estimated over multiple floors integrating different sensor modalities, namely Wi-Fi, iBeacons, barometer, step-detection and turn-detection.
+The system was tested by omitting any time-consuming calibration process and starting with a uniform distribution instead of a well known pedestrian location. 
+The evaluation shows that adding prior knowledge is able to improve the localisation, even for unpredictable behaviour, faulty measurements and for poorly chosen system parameters. 
 \end{abstract}