diff --git a/tex_review/chapters/conclusion.tex b/tex_review/chapters/conclusion.tex
index 40bffec..6d6314f 100644
--- a/tex_review/chapters/conclusion.tex
+++ b/tex_review/chapters/conclusion.tex
@@ -1,15 +1,24 @@
 \section{Conclusion}
 
-\commentByToni{Wie wirkt sich das jetzt auf ein generelles Gebäude aus?}
-
 %what you have seen
-Within this work we provided an extensive overview of our smartphone-based indoor localization system. 
+Within this work we provided an extensive overview of our smartphone-based indoor localization system, \add{providing both, previous advances and novel contributions.}
 The thorough evaluation demonstrated the good performance under multiple scenarios within a complex environment. 
 The system is able to handle problems like sample impoverishment and multimodal densities, occurring through the use of a particle filtering scheme. 
-The main advantage of our approach is its suitability for practical use.
+\add{Based on the good results in this challenging scenario, we believe that our solution can be adapted to many other public buildings and environments, resulting in a very generally usable  solution for self-localization of pedestrians using smartphones.
+Previous versions of the system have already proven themselves in other, more modern buildings, which supports this claim to general use.}
+
+%novel stuff
+\add{Thanks to the novel contributions presented, we have been able to further increase the robustness and accuracy of the system.
+To a large extent, this was achieved by using the navigation mesh. 
+It allows to map continuous movements and enables to reduce the map sizes to only a few megabytes for a complete building. 
+The problem of sample impoverishment can be addressed easily by incorporating the here presented method onto the state transition of the particle filter.  
+In combination with the threshold-based activity recognition, both methods further enhance the robustness.}
+Given the improvements above and those achieved in previous works, the main advantage of our approach is its suitability for practical use.
 Compared to other state-of-the-art solutions, the setup time is only a few hours and does not require any expert knowledge or hardware. 
+\add{The system should require as little manual effort as possible. 
+This is mainly achieved by the optimization scheme, providing all necessary parameters for the Wi-Fi model.}
 The localization runs solely an a commercial smartphone, thus no connection to a server or the Wi-Fi infrastructure is required.
-By using navigation meshes we are able to reduce the map sizes to only a few megabytes for a complete building. 
+
 
 Nevertheless, there is still room for further improvements and future work. 
 Through the change from a graph to a mesh, we lost the ability to easily find the shortest path for navigation purposes as described in \cite{Ebner-16}. 
diff --git a/tex_review/chapters/experiments.tex b/tex_review/chapters/experiments.tex
index b496adb..c19b9d4 100644
--- a/tex_review/chapters/experiments.tex
+++ b/tex_review/chapters/experiments.tex
@@ -478,8 +478,4 @@ It does not provide a smooth estimated path, since it depends more on an accurat
 At the end, in the here shown examples we only searched for a global maxima, even though the KDE approach opens a wide range of other possibilities for finding a best estimate. 
 \add{A detailed examination of the runtime performance of the used estimation methods in comparison to the state-of-the-art can be found in \cite{Bullmann-18}.}
 
-\commentByToni{Diskussion, wie die Contributions uns jetzt geholfen haben. Nochmal zusammengefasst.}
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-
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