added images of our apps to experiments

tex_review/bare_conf.tex

@@ -98,7 +98,7 @@ $^{2}$ \quad University of Siegen - Pattern Recognition Group; marcin.grzegorzek
 }
 
 
-\graphicspath{{gfx/}{gfx/groundTruth/}{gfx/wifiOptGlobalFloor/}{gfx/errorOverTimeWalk3/}{gfx/estimationPath2/}{gfx/optimization/}{gfx/optimization/side/}{gfx/transEval/}}
+\graphicspath{{gfx/}{gfx/groundTruth/}{gfx/wifiOptGlobalFloor/}{gfx/errorOverTimeWalk3/}{gfx/estimationPath2/}{gfx/optimization/}{gfx/optimization/side/}{gfx/transEval/}{gfx/apps/}}
 
 \input{chapters/abstract}
 

tex_review/chapters/experiments.tex

@@ -12,9 +12,16 @@ In the middle of the building is an outdoor area, which is only accessible from
 Due to objects like exhibits, cabinets or signs not all positions within the building were freely accessible.}
 For the sake of simplicity we did not incorporate such knowledge into the floorplan. 
 Thus, the floorplan consists only of walls, ceilings, doors, windows and stairs.
-It was created using our 3D map editor software based on architectural drawings from the 1980s. 
+It was created using our 3D map editor software (see fig. \ref{fig:mapeditor}) based on architectural drawings from the 1980s. 
 \add{The mesh is then created automatically, which only takes a few seconds to compute.}
 
+\begin{figure}[t]
+		\centering
+		\includegraphics[width=\textwidth]{gfx/apps/editor_light.png}
+        \caption{\add{The 3D map editor we developed to create the floorplans. This screenshot shows the ground level of the building. The window is split into toolbar (left), layers (upper right), parameters of current selection (lower right), drawing mode (upper center) and 3D view (lower center).}}
+		 \label{fig:mapeditor}
+\end{figure}
+
 %wie haben wir die ap aufgehängt 
 \add{As described in section \ref{sec:wifi} we used \SI{42}{} WEMOS D1 mini to provide a \docWIFI{} infrastructure throughout the building.
 The distribution of the beacons on ground floor can be seen in fig. \ref{fig:apfingerprint} (black dots) as well as the references (fingerprints) for optimization. 
@@ -29,7 +36,7 @@ So there were no prior requirements how to place a single beacon exactly and the
 Considering all the above, the beacons were placed more or less freely and to the best of our knowledge.} 
 
 \add{A very similar approach was chosen for placing the fingerprints. 
-The positions of the fingerprints are set within our 3D map editor software by simply dragging the fingerprinting icon on the desired position or by entering the position manually. 
+The positions of the fingerprints are set within our 3D map editor (see fig. \ref{fig:mapeditor}) software by simply dragging the fingerprinting icon on the desired position or by entering the position manually. 
 The reference points were placed every \SI{3}{\meter} to \SI{7}{\meter} from each other, however as can be seen in fig. \ref{fig:apfingerprint} not very accurately. 
 A perfect distance between the single points is not a crucial factor for the optimization and thus we consider such an accurate approach to be pointless.
 Furthermore, it is not easy to adopt the exact position to take the reference measurements in the building later on. 
@@ -38,7 +45,7 @@ Of course, this could be done with appropriate hardware (e.g. laser-scanner), bu
 \add{Summing up the above, the following initial steps are required to utilize our localization system to a building: 
 \begin{enumerate}
 	\item Acquiring a blueprint or architectural drawing of the building including at minimum the walls and stairs of the respective floors. 
-	\item Based on this 2D drawing, the floorplan is created using our 3D map editor (cf. fig. \ref{fig:museumMap}). This requires manual effort, comparable to software like Inkscape or FreeCAD. 
+	\item Based on this 2D drawing, the floorplan is created using our 3D map editor (cf. fig. \ref{fig:mapeditor}). This requires manual effort, comparable to software like Inkscape or FreeCAD. 
 	\item If necessary, creating or improving the Wi-Fi infrastructure by plugging in beacons to available power sockets and write all MAC-addresses into a whitelist.
 	%\item Store floorplan and whitelist of MAC-addresses onto the smartphone. 
 	\item Record the reference measurements based on the reference positions given in the floorplan. 
@@ -50,7 +57,7 @@ Step 1 and 2 were conducted off-site.
 The blueprint was initially provided by the director of the museum as digital photography. 
 Creating the floorplan including walls and stairs took us approximately \SI{40}{\minute} and is then stored onto the smartphone after creation. 
 Adding knowledge like semantic information such as room numbers would of course take additional time.
-All other steps were performed on-site using our smartphone app for localization. 
+All other steps were performed on-site using our smartphone app for localization, which can be seen in fig. \ref{fig:yasmin}. 
 As the museum did not provide any Wi-Fi infrastructure, we installed the \SI{42}{} beacons as explained above. 
 Thanks to the great support of the museum's janitor, this step took only \SI{30}{\minute}, as he was well aware of all available power outlets and also helped plugging them in.
 After that, each of the \SI{133}{} reference points was scanned 30 times ($\approx \SI{25}{\second}$ scan time) using a Motorola Nexus 6 at \SI{2.4}{GHz}. 
@@ -61,10 +68,27 @@ Thus the above provided times were measured for a pure localization installation
 Nevertheless, we believe that an on-site setup-time of less then \SI{120}{\minute} is a big step for the practicability of localization systems. 
 In addition, the above steps do not require a high level of detail in their execution, which should also allow unbiased persons to set up the system.}
 
+\begin{figure}[t]
+	\centering
+	\begin{subfigure}{0.45\textwidth}
+		\includegraphics[width=\textwidth]{gfx/apps/yasmin.png}
+        \caption{Localization App}
+ 		\label{fig:yasmin}
+    \end{subfigure}
+	\hspace{1cm}
+	\begin{subfigure}{0.45\textwidth}
+		\includegraphics[width=\textwidth]{gfx/apps/simple.png}
+        \caption{Simple Recording App}
+		 \label{fig:simple}
+    \end{subfigure}
+
+    \caption{\add{The two mobile applications developed for Android. The localization app in (a) is used to record the Wi-Fi reference measurements based on the positions provided by the floorplan. In this screenshot the dialog for recording them is visible. The app also implements the here presented approach and can thus be used for localization. However, for the utilized experiments we used a simpler client (b) allowing for user input like a ground truth or activity button.}}
+    \label{fig:applications}
+\end{figure}
 
 \add{As mentioned, the here presented localization system was implemented as an Android App.
 It was written in high performant C++ code, enabling to run completely on the smartphone and thus not requiring any connection to a server.
-However, since the experiments required a lot of different information to evaluate the methods, a second, very simple application was developed to record them.
+However, since the experiments required a lot of different information to evaluate the methods, a second, very simple application was developed to record them (see fig. \ref{fig:simple}).
 It implements the standard Android sensor functionalities and provides a very simple user interface so that even non-technical users can use it.} 
 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. 
@@ -111,7 +135,7 @@ Finally, the respective estimation methods are discussed in section \ref{sec:eva
 		\input{gfx/transEval/grid_180_final.eps_tex}
         \caption{Graph after 180 steps}
  		\label{fig:transitionEval:d}
-    \end{subfigure}
+\end{subfigure}
 
     \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}

tex_review/chapters/misc.tex

@@ -54,6 +54,7 @@ However, we believe that such a combination of two independent filters is not ne
 
 %neue methode:
 \add{For the simplified version we draw a number of $N_{\probGrid}$ locations $\mPosVec_{\probGrid} = (x,y,z)^T$ uniformly from the underlying navigation mesh.
+%TODO: sollten wir noch erläutern WIE man zieht? Also zieht erst cummulative, basierend auf der größe der Triangle ein Triangle und dann über barizentrische koordinaten eine position innerhalb des dreiecks unifom.
 Based on this locations we then approximate $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ as described in section \ref{sec:wifi}. 
 This results in a set of probabilities associated with $\mPosVec_{\probGrid}$, we call probability grid $\probGrid_{t, \text{wifi}}$.
 It is important to notice, that $\probGrid_{t, \text{wifi}}$ is newly created in every filter update, independently of the filter's current particle set.