added gfx

tex/chapters/experiments.tex

@@ -36,9 +36,12 @@ We therefore expect the localisation process to perform generally worse compared
 However, incorporating prior knowledge and smoothing will often compensate for those poorly chosen system parameters.
 
 For the  filtering we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$ as uncertainties, both growing with each measurement's age.
-While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters are determined automatically (see \ref{sec:sensBaro}). 
-The step size $\mStepSize$ for the transition was configured to be \SI{70}{\centimeter} with an allowed derivation of \SI{10}{\percent}. The heading deviation in \refeq{eq:transSimple}, \refeq{eq:transShortestPath} and \refeq{eq:transMultiPath} was \SI{25}{\degree}.
-Edges departing from the pedestrian's destination are downvoted using $\mUsePath = 0.9$.
+While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters are determined automatically (see \ref{sec:eval}). 
+The step size $\mStepSize$ for the transition was configured to be \SI{70}{\centimeter} with an allowed derivation of \SI{10}{\percent}. The heading deviation was set to \SI{25}{\degree}. 
+Edges departing from the pedestrian's destination are downvoted using $\mUsePath = 0.9$. 
+For smoothing we set $\sigma^2_{\text{turn}} = \SI{5}{\degree}$ and $\sigma^2_z = \SI{0.25}{\centimeter}$.
+If walking or unknown are the current activities, $ \mu_{\text{step}} = \SI{0.7}{\meter}$, $ \mu_{\text{step}} = \SI{0.5}{}$ and $ \mu_z = \SI{0.0}{\meter}$ are used. 
+Walking upstairs sets $ \mu_{\text{step}} = \SI{0.4}{\meter}$, $ \sigma_{\text{step}}^2 = \SI{0.2}{}$ and $ \mu_z = \SI{-0.3}{\meter}$, otherwise $ \mu_{\text{step}} = \SI{0.5}{\meter}$, $ \sigma_{\text{step}}^2 = \SI{0.3}{}$ and $ \mu_z = \SI{0.3}{\meter}$ for walking downstairs.
 
 % all paths we evaluated
 \begin{figure}
@@ -55,7 +58,7 @@ Edges departing from the pedestrian's destination are downvoted using $\mUsePath
 By adding the activity recognition to the system of \cite{Ebner-16}, we are able to further improve the overall localisation results. 
 The approximation error decreases by an average of \SI{66666}{\centimeter} for all 4 paths on 10 MC runs.
 Due to this additional knowledge, the state transition samples mostly depending upon the current activity and therefore limits the possibility of false floor changes.
-Fig. \ref{dd} shows the recognized activities for path 4 using the Nexus 6.
+Fig. \ref{fig:activityRecognition} shows the recognized activities for path 4 using the Nexus 6. 
 Despite a short misdetection caused by faulty pressure readings, the recognition can be considered to be very robust and accurate.
 
 %Fixed Interval Smoothing
@@ -73,12 +76,23 @@ Now, the positional error along all 4 paths could be improved from \SI{}{} to \S
 %Similar outcomes can be observed by adding a resampling step at the end of every smoothing iteration. 
 
 However, BS still outperforms the FBS by an average of \SI{}{} on all 4 paths using the same number of particles and \SI{500}{} sample realisations. 
-A visual example comparing both smoothing methods on path 4 is illustrated in fig. \ref{}.
+A visual example comparing both smoothing methods on path 4 is illustrated in fig. \ref{fig:intcomp}.
 Another advantage of BS over FBS, is the ability to still improve the filtering results even while reducing the number of particles radical. 
 For example \SI{50}{} particles and \SI{25}{} sample realisations are providing reliable estimations similar to above experiments, though the risk of losing track is higher.
+\begin{figure}
+	\input{gfx/eval/interval_path4_comp/path4_interval}
+	\caption{Comparison between FBS (red) and BS (blue) on path 4 (black). Both were approximated using \SI{2500}{} particles and \SI{500}{} sample realisations for BS. The measurements were recorded using the Nexus 6.}
+	\label{fig:intcomp}
+\end{figure}
 
+\begin{figure}
+	\input{gfx/eval/interval_path3_bad/path3_interval}
+	\caption{A situation where BS smoothing (blue) was not able to improve the filtering results (green). Two main factors are causing this: an initial position within a detached room and inaccurate pressure readings given by the Galaxy S5.}
+	\label{fig:intervalbad}
+\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. 
-For example fig. \ref{} depicts such a situation for path 3 using BS and measurements provided by the Galaxy S5.
+For example fig. \ref{fig:intervalbad} depicts such a situation for path 3 using BS and measurements provided by the Galaxy S5. 
+Here, the estimation was not able to change floors correctly due to faulty pressure readings. Additionally, the initial position was located within a detached room.
 This shows that the smoothing results are of course highly depend upon the filtering performance.
 
 At next, we discuss the advantages and disadvantages of utilizing FBS and BS as fixed-lag smoother. 
@@ -87,7 +101,10 @@ Especially interesting in this context are small lags $\tau < 10$ considering fi
 
 %as seen fbs war im fixed interval schon nicht so gut, im lag ist sein einfluss vernachlässigbar. optische und error technische verbesserung sind kaum vorhanden. lediglich eine verbesserung von  deshalb konzentrieren wir uns bei der diskussion auf den BS. trotzdem verschlechtert sich das ergebniss aber auch nicht. die verbesserung ist nur nicht so signifikant wie bei bs
 
-Fig. \ref{} illustrates the estimation results for path 4 using \SI{2500}{particles}, \SI{50}{sample realisations} for BS and a fixed-lag $\tau = 5$. 
+%wie gut ist fixed-lag mit einem lag = 5. was fällt so auf?
+Fig. \ref{} illustrates the estimation results for path 4 using \SI{2500}{particles}, \SI{500}{sample realisations} for BS and a fixed-lag $\tau = 5$. 
+
+%lag vergrößern was passiert beschreiben
 
 fixed-lag reduces the error about... however, as seen in fig. \ref{} ist der bloße error nicht unbedingt ausschlaggebend für die verbesserung. fast immer liefert smoothing pfade die realistischer sind, aber die error erhöhen. 
 
@@ -103,7 +120,7 @@ bei weniger partikeln bringt fixed-lag und fixed-interval smoothing im verhältn
 
 %beispiel multimodalität
 
-%ein paar worte zur berechenzeit.
+%Tabelle mit spalten interval partikel, lag partikel; spalten: filter, BS, FBS
 
 %Evaluation:
 %\begin{itemize}