diff --git a/competition/tex/chapters/introduction.tex b/competition/tex/chapters/introduction.tex
index c417540..676a3a0 100644
--- a/competition/tex/chapters/introduction.tex
+++ b/competition/tex/chapters/introduction.tex
@@ -8,7 +8,14 @@ Compared to many other systems, we avoid any time-consuming fingerprinting and c
 All calculations are computed in real time on a commercial smartphone, in most of our examples this is the Motorola Nexus 6 or the Samsung Galaxy S5. 
 The system is implemented in C++ using the Qt framework and OpenCL. 
 
-An overview of all involved components and the sensor fusion procedure can be seen in fig. \ref{}. 
+  \begin{figure}
+	\centering
+	\includegraphics[width=\linewidth]{gfx/info_graphic}
+	\label{fig:sysoverview}
+	\caption{A broad overview of the localization and navigation system presented. The sensor measurements and calculations are all provided by the smartphone itself. The floorplan is known beforehand but extensively used every time step.}
+  \end{figure}%  
+
+An overview of all involved components and the sensor fusion procedure can be seen in fig. \ref{fig:sysoverview}. 
 Here, the smartphone provides all necessary measurements and no additional device is needed.
 The readings of all those sensors are fused using recursive density estimation, directly on the phone:
 %		
@@ -46,8 +53,6 @@ where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a seri
 The recursive density estimation of eq. \eqref{eq:recursiveDensity} is implemented using a particle-filter with the state transition as proposal density.
 This ensures valid position estimations even if a sensor is defect or is not provided by the smartphone itself.
 
-
-
 \section{Prior Arrangements}
 System setup is very easily and no fingerprinting is required.
 	\begin{figure}[h!]
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