added paths for fig 6

tex/chapters/experiments.tex

@@ -50,7 +50,7 @@ Fig. compares optimized ap vs real positions for the ground level, thus we only
 \begin{figure}[bt]
 	\centering
   \includegraphics[width=0.9\textwidth]{gfx/floorplanDummy.png}
-	\caption{Floorplan Dummy}
+	\caption{Position of Ap's optimized with global and per floor and real.}
 	\label{fig:apfingerprint}
 \end{figure}
 
@@ -66,7 +66,7 @@ Fig. compares optimized ap vs real positions for the ground level, thus we only
 \begin{figure}[ht]
 	\centering
   	\includegraphics[width=0.9\textwidth]{gfx/floorplanDummy.png}
-	\caption{Floorplan Dummy}
+	\caption{All conducted walks.}
 	\label{fig:floorplan}
 \end{figure}
 %
@@ -250,11 +250,11 @@ In contrast, a KDE-based approach for estimation is able to resolve multimodalit
 It does not always provide the lowest error, since it depends more on an accurate sensor model then a weighted average approach, but is very suitable as a good indicator about the real performance of a sensor fusion system. 
 At the end, in the here shown examples we only searched for a global maxima, even though this approach opens a wide range of other possibilities for finding a best estimate.
 
-\begin{figure}[t]
+\begin{figure}[bt]
 	\centering
-	\input{gfx/errorOverTimeWalk1/errorOverTime.tex}
-	\caption{Error development over time of a single Monte Carlo run of the walk calculated between estimation and ground truth. Between \SI{230}{\second} and \SI{290}{\second} to pedestrian was not moving.}
-	\label{}
+  \includegraphics[width=0.9\textwidth]{gfx/floorplanDummy.png}
+	\caption{Estimation results of walk 2 using the KDE method (orange) and the weighted-average (blue).}
+	\label{fig:apfingerprint}
 \end{figure}