added path 2 for eval instead path 3

tex/chapters/experiments.tex

@@ -88,9 +88,15 @@ We deployed 10 MC runs using \SI{2500}{} particles for approximation and a multi
 Now, the positional average error along all 4 paths using the Nexus and the Galaxy could be improved from \SI{2.08}{\meter} to \SI{1.37}{\meter}. 
 Using the same number of particles and $500$ sample realisations the BS performs with an average error of \SI{2.21}{\meter} for filtering and  \SI{1.51}{\meter} for smoothing.
 The difference between both filtering steps is of course based upon the randomized behaviour of the respective probabilistic models. 
-It is interesting to note, that the positional error is very similar for both used smartphones, although the approximation error varies greatly as we will see later. 
+It is interesting to note, that the positional error is very similar for both used smartphones, although the approximation error varies greatly. 
+Using the FBS, the Galaxy donates an average approximation error of \SI{4.03}{\meter} by filtering with \SI{7.74}{\meter}. 
+In contrast the Nexus 6 filters at \SI{5.11}{\meter} and results in \SI{3.87}{\meter} for smoothing.
+The BS has a similar improvement rate.
 
 A visual example of the smoothing outcome for path 3 is illustrated in fig. \ref{fig:int_path3_a}.
+It can be clearly seen, how the smoothing compensates for the faulty detected floor change using future knowledge. 
+ 
+
 The estimation of BS looks way more realistic and adapts better to the ground truth path.
 %However, in this particular example the FBS starts at an earlier position (cf. fig. \ref{fig:intcomp} seg. 2), better handling the initial uniform distribution. 
 %Another advantage of BS over FBS, is the ability to still improve the filtering results even while reducing the number of particles radical. 
@@ -98,16 +104,16 @@ The estimation of BS looks way more realistic and adapts better to the ground tr
 
 \begin{figure}
 	\begin{subfigure}{0.175\textwidth}
-		\input{gfx/eval/interval_path3_good/path3_interval_good}
+		\input{gfx/eval/interval_path2_good/path2_interval_good}
 		\caption{}
-		\label{fig:int_path3_a}
+		\label{fig:int_path2_a}
 	\end{subfigure}
 	\begin{subfigure}{0.175\textwidth}
-		\input{gfx/eval/interval_path3_bad/path3_interval}
+		\input{gfx/eval/interval_path2_bad/path2_interval}
 		\caption{}
-		\label{fig:int_path3_b}
+		\label{fig:int_path2_b}
 	\end{subfigure}
-	\caption{a) Exemplary comparison between BS (blue) and filtering (green) using 2500 particles and 500 sample realisations. b) A situation where BS smoothing was not able to improve the filtering results. Two main factors are causing this: an initial position within a detached room and inaccurate pressure readings given by the Galaxy S5.}
+	\caption{a) Exemplary results for path 2 where BS (blue) and filtering (green) using 2500 particles and 500 sample realisations. b) A situation where BS smoothing was not able to improve the filtering results. Two main factors are causing this: an initial position within a detached room and inaccurate pressure readings given by the Galaxy S5.}
 \label{fig:int_path3_comp}
 \end{figure}
 Despite the very good outcomes provided by both interval smoother, there are some rare situations in which smoothing does not improve the filtered estimation or even improves the visual path. 
@@ -118,6 +124,9 @@ This shows that the smoothing results are of course highly depend upon the filte
 At next, we discuss the advantages and disadvantages of utilizing FBS and BS as fixed-lag smoother. 
 Compared to fixed-interval smoothing, timely errors are now of higher importance due to an interest on real-time localization. 
 Especially interesting in this context are small lags $\tau < 10$ considering filter updates near \SI{500}{\milli\second}.
+
+%Path 4 grafik mit fixed-lags
+
 %
 %wie gut ist fixed-lag mit einem lag = 5. was fällt so auf?
 Fig. \ref{fig:lag_error_path4} illustrates the different approximation errors alongside path 4 using $500$ particles, \SI{100}{sample realisations} for BS and a fixed-lag $\tau = 5$.