small changes in abstract and intro

tex/chapters/abstract.tex

@@ -6,6 +6,6 @@ In order to create such paths, we present a method that assigns an importance-fa
 The human movement is then modelled by moving along adjacent nodes into the most proper walking-direction. 
 To enable 3D localisation, 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- and turn-detection.
-The system was tested by omitting any time-consuming fingerprinting and calibration process and starts with a uniform distribution over the whole building instead of a well known pedestrian location. 
+The system was tested by avoiding any time-consuming fingerprinting and calibration process and starts with a uniform distribution over the whole building instead of a well known pedestrian location. 
 The evaluation shows that adding prior knowledge is able to improve the localisation, even under unpredictable behaviour, faulty measurements and poorly chosen system parameters. 
 \end{abstract}

tex/chapters/introduction.tex

@@ -37,8 +37,8 @@ Despite very good results, the system presented in \cite{Ebner-15} suffers from
 
 First, the transition model of our previous approach uses discrete floor-changes.
 Although the overall system provides viable results, it does not resemble real-world floor changes.
-Especially the barometer is affected due to its continuous pressure measurements.
-The discrete model prevents the barometer's full potential.
+Especially the barometric model is affected due to its continuous pressure measurements.
+The discrete model limits the barometer's full potential.
 It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions.
 To address this problem we extended the graph by adding realistic stairs, allowing a step-wise transition in the $z$-direction.