Small fixes + graphics

tex_review/chapters/abstract.tex

@@ -20,7 +20,7 @@ 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} \add{\SI{2500}{\square\meter}} building. 
+\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. 
 }

tex_review/chapters/experiments.tex

@@ -16,7 +16,7 @@ It was created using our 3D map editor software based on architectural drawings
 Sensor measurements are recorded using a simple mobile application that implements the standard Android sensor functionalities.
 As smartphones we used either a Samsung Note 2, Google Pixel One or Motorola Nexus 6.
 The computation of the state estimation as well as the \docWIFI{} optimization are done offline using an Intel Core i7-4702HQ CPU with a frequency of \SI{2.2}{GHz} running \add{\SI{8}{threads} on \SI{4}{cores}} and \SI{16}{GB} main memory. 
-However, similar to our \add{previously presented systems}, the setup is able to run completely on commercial smartphones as it written in high performant C++ code \cite{torres2017smartphone}. 
+However, similar to our \add{previously presented system}, the setup is able to run completely on commercial smartphones as it \add{is} written in high performant C++ code \cite{torres2017smartphone}. 
 %Sensor measurements are recorded using a simple mobile application that implements the standard Android SensorManager. 
 
 The experiments are separated into four sections:
@@ -58,7 +58,7 @@ Finally, the respective estimation methods are discussed in section \ref{sec:eva
  		\label{fig:transitionEval:d}
     \end{subfigure}
 
-    \caption{Simple staircase scenario to compare the graph-based model with the navigation mesh. All units are given in meter. The black line indicates the current position and the green line gives the estimated path until 25 or 180 steps, both using weighted average. The particles are colored according to their height. A pedestrian walks up and down the stairs several times in a row. After 25 steps, both methods produce good results, although there are already some outliers (blue particles). After 180 steps, the outliers using the graph have multiplied, leading to a multimodal situation. In contrast, the mesh offers the possibility to remove particles that hit a wall and can thus prevent such a situation.}
+    \caption{Simple staircase scenario to compare the \add{old} graph-based model with the \add{new} navigation mesh. All units are given in meter. The black line indicates the current position and the green line gives the estimated path until 25 or 180 steps, both using weighted average. The particles are colored according to their \add{$z$-coordinate}. A pedestrian walks up and down the stairs several times in a row. After 25 steps, both methods produce good results, although there are already some outliers (blue particles). After 180 steps, the outliers using the graph have multiplied, leading to a multimodal situation. In contrast, the mesh offers the possibility to remove particles that hit a wall and can thus prevent such a situation.}
     \label{fig:transitionEval}
 \end{figure}
 
@@ -183,7 +183,7 @@ Further evaluations and discussions regarding the here used optimization can be
 		{\input{gfx/groundTruth/gt_oben_final.eps_tex}}
         \caption{Second floor}
     \end{subfigure}
-	\caption{All conducted walks within the building. The arrows indicate the running direction and a cross marks the end. For a better overview we have divided the building into 3 floors. However, each floor consists of different high levels. They are separated from each other by different shades of grey, dark is lower then light.}
+	\caption{All conducted walks within the building. The arrows indicate the running direction and a cross marks the end. For a better overview we have divided the building into three floors, \add{which are connected by four stairs (numbered 1--4)}. However, each floor consists of different high levels. They are separated from each other by different shades of grey, dark is lower than light.}
 	\label{fig:floorplan}
 \end{figure}
 %

tex_review/chapters/introduction.tex

@@ -28,7 +28,7 @@ Many unknown quantities, like the walls definitive material or thickness, make i
 Additionally, \del{most wireless} \add{many of these} approaches are based on a line-of-sight assumption. 
 Thus, the performance will be even more limited due to the irregularly shaped spatial structure of such buildings. 
 Our approach tries to avoid those problems using an optimization scheme for Wi-Fi based on a \del{few} \add{set of} reference measurements.  
-We distribute a \del{small number} \add{set} of \del{simple} \add{small (\SI{2.8}{\centi\meter} x \SI{3.5}{\centi\meter})} and cheap \add{($\sim \SI{10}{\$}$)} \docWIFI{} beacons over the whole building \add{to ensure a reasonable coverage} and instead of measuring their position \add{and necessary parameters, we use our optimization scheme, initially presented in \cite{Ebner-17}}.
+We distribute a \del{small number} \add{set} of \del{simple} \add{small (\SI{2.8}{\centi\meter} x \SI{3.5}{\centi\meter})} and cheap \add{($\approx \SI{10}{\$}$)} \docWIFI{} beacons over the whole building \add{to ensure a reasonable coverage} and instead of measuring their position \add{and necessary parameters, we use our optimization scheme, initially presented in \cite{Ebner-17}}.
 
 \add{An optimization scheme is able to compensate for wrongly measured access point positions, inaccurate building plans or other knowledge necessary for the Wi-Fi component.
 }