Small fixes + graphics

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}
 %