Merge branch 'master' of https://git.frank-ebner.de/FHWS/IPIN2018

tex_review/chapters/abstract.tex

@@ -20,7 +20,7 @@ To stress our system, we have chosen a very challenging test scenario.
 All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. 
 The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{310}{\meter} length and \SI{10}{\minute} duration.
 It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. 
-\del{Our advanced} \add{The introduced} filtering methods allow for a real fail-safe system, while the optimization scheme enables a setup-time of under \SI{120}{\minute} for the \del{complete} \add{\SI{2500}{\square\meter}} building. 
+\del{Our advanced} \add{The introduced} filtering methods allow for a real fail-safe system, while the optimization scheme enables a setup-time of under \SI{120}{\minute} for the \del{complete building}  \add{building's \SI{2500}{\square\meter} walkable area}. 
 %We are able to resolve sample impoverishment whenever it occurs and thus provide a real fail-safe system. 
 %finally compare the standard weighted-average estimator with our kernel density approach. 
 }

tex_review/chapters/experiments.tex

@@ -16,7 +16,7 @@ It was created using our 3D map editor software based on architectural drawings
 Sensor measurements are recorded using a simple mobile application that implements the standard Android sensor functionalities.
 As smartphones we used either a Samsung Note 2, Google Pixel One or Motorola Nexus 6.
 The computation of the state estimation as well as the \docWIFI{} optimization are done offline using an Intel Core i7-4702HQ CPU with a frequency of \SI{2.2}{GHz} running \add{\SI{8}{threads} on \SI{4}{cores}} and \SI{16}{GB} main memory. 
-However, similar to our \add{previously presented systems}, the setup is able to run completely on commercial smartphones as it written in high performant C++ code \cite{torres2017smartphone}. 
+However, similar to our \add{previously presented system}, the setup is able to run completely on commercial smartphones as it \add{is} written in high performant C++ code \cite{torres2017smartphone}. 
 %Sensor measurements are recorded using a simple mobile application that implements the standard Android SensorManager. 
 
 The experiments are separated into four sections:
@@ -58,7 +58,7 @@ Finally, the respective estimation methods are discussed in section \ref{sec:eva
  		\label{fig:transitionEval:d}
     \end{subfigure}
 
-    \caption{Simple staircase scenario to compare the graph-based model with the navigation mesh. All units are given in meter. The black line indicates the current position and the green line gives the estimated path until 25 or 180 steps, both using weighted average. The particles are colored according to their height. A pedestrian walks up and down the stairs several times in a row. After 25 steps, both methods produce good results, although there are already some outliers (blue particles). After 180 steps, the outliers using the graph have multiplied, leading to a multimodal situation. In contrast, the mesh offers the possibility to remove particles that hit a wall and can thus prevent such a situation.}
+    \caption{Simple staircase scenario to compare the \add{old} graph-based model with the \add{new} navigation mesh. All units are given in meter. The black line indicates the current position and the green line gives the estimated path until 25 or 180 steps, both using weighted average. The particles are colored according to their \add{$z$-coordinate}. A pedestrian walks up and down the stairs several times in a row. After 25 steps, both methods produce good results, although there are already some outliers (blue particles). After 180 steps, the outliers using the graph have multiplied, leading to a multimodal situation. In contrast, the mesh offers the possibility to remove particles that hit a wall and can thus prevent such a situation.}
     \label{fig:transitionEval}
 \end{figure}
 
@@ -183,7 +183,7 @@ Further evaluations and discussions regarding the here used optimization can be
 		{\input{gfx/groundTruth/gt_oben_final.eps_tex}}
         \caption{Second floor}
     \end{subfigure}
-	\caption{All conducted walks within the building. The arrows indicate the running direction and a cross marks the end. For a better overview we have divided the building into 3 floors. However, each floor consists of different high levels. They are separated from each other by different shades of grey, dark is lower then light.}
+	\caption{All conducted walks within the building. The arrows indicate the running direction and a cross marks the end. For a better overview we have divided the building into three floors, \add{which are connected by four stairs (numbered 1--4)}. However, each floor consists of different high levels. They are separated from each other by different shades of grey, dark is lower than light.}
 	\label{fig:floorplan}
 \end{figure}
 %
@@ -216,26 +216,23 @@ In contrast, the $D_\text{KL}$-based method extends the transition and thus uses
 We set $l_\text{max} =$ \SI{-75}{dBm} and $l_\text{min} =$ \SI{-90}{dBm}.
 For a better overview, we only used the KDE-based estimation, as the errors compared to the weighted-average estimation differ by only a few centimeter.
 
-\newcommand{\STAB}[1]{\begin{tabular}{@{}c@{}}#1\end{tabular}}
-
 \begin{table}[t]
 	\centering
-	\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
-		\hline
-		Method & \multicolumn{3}{c|}{none} & \multicolumn{3}{c|}{simple} & \multicolumn{3}{c|}{$D_\text{KL}$}\\
-		\hline
-		  & $\bar{x}$ & $\bar{\sigma}$ & $\tilde{x}_{75}$ & $\bar{x}$ & $\bar{\sigma}$ & $\tilde{x}_{75}$ & $\bar{x}$ & $\bar{\sigma}$ & $\tilde{x}_{75}$ \\ 
-		\hline \hline
-     	walk 0 & \SI{13.4}{\meter} & \SI{11.2}{\meter} & \SI{22.6}{\meter} & \SI{7.1}{\meter} & \SI{6.6}{\meter} & \SI{9.4}{\meter} & \SI{5.8}{\meter} & \SI{4.9}{\meter} & \SI{7.3}{\meter} \\ \hline
-		walk 1 & \SI{3.2}{\meter} & \SI{2.4}{\meter} & \SI{4.1}{\meter} & \SI{3.2}{\meter} & \SI{2.6}{\meter} & \SI{4.0}{\meter} & \SI{3.8}{\meter} & \SI{3.2}{\meter} & \SI{4.6}{\meter} \\ \hline
-		walk 2 & \SI{8.3}{\meter} & \SI{4.1}{\meter} & \SI{10.9}{\meter} & \SI{3.6}{\meter} & \SI{2.3}{\meter} & \SI{4.9}{\meter} & \SI{3.6}{\meter} & \SI{2.3}{\meter} & \SI{4.8}{\meter} \\ \hline
-		walk 3 & \SI{7.0}{\meter} & \SI{5.9}{\meter} & \SI{13.5}{\meter} & \SI{5.4}{\meter} & \SI{4.7}{\meter} & \SI{7.7}{\meter} & \SI{4.8}{\meter} & \SI{4.3}{\meter} & \SI{6.5}{\meter} \\
-		\hline
+	\begin{tabular}{rrrrcrrrcrrr}
+		\toprule
+		 & \multicolumn{3}{c}{none} &  \phantom{abc} & \multicolumn{3}{c}{simple} & \phantom{abc} &  \multicolumn{3}{c}{$D_\text{KL}$} \\
+		 \cmidrule{2-4}  \cmidrule{6-8} \cmidrule{10-12}
+		 & \multicolumn{1}{c}{$\bar{x}$} & \multicolumn{1}{c}{$\bar{\sigma}$} & \multicolumn{1}{c}{$\tilde{x}_{75}$} && \multicolumn{1}{c}{$\bar{x}$} & \multicolumn{1}{c}{$\bar{\sigma}$} & \multicolumn{1}{c}{$\tilde{x}_{75}$} && \multicolumn{1}{c}{$\bar{x}$} & \multicolumn{1}{c}{$\bar{\sigma}$} & \multicolumn{1}{c}{$\tilde{x}_{75}$} \\ 
+		\midrule 
+     	walk 0 & \SI{13.4}{\meter} & \SI{11.2}{\meter} & \SI{22.6}{\meter} && \SI{7.1}{\meter} & \SI{6.6}{\meter} & \SI{9.4}{\meter} && \SI{5.8}{\meter} & \SI{4.9}{\meter} & \SI{7.3}{\meter} \\ 
+		walk 1 & \SI{3.2}{\meter}  & \SI{2.4}{\meter}  & \SI{4.1}{\meter}  && \SI{3.2}{\meter} & \SI{2.6}{\meter} & \SI{4.0}{\meter} && \SI{3.8}{\meter} & \SI{3.2}{\meter} & \SI{4.6}{\meter} \\ 
+		walk 2 & \SI{8.3}{\meter}  & \SI{4.1}{\meter}  & \SI{10.9}{\meter} && \SI{3.6}{\meter} & \SI{2.3}{\meter} & \SI{4.9}{\meter} && \SI{3.6}{\meter} & \SI{2.3}{\meter} & \SI{4.8}{\meter} \\ 
+		walk 3 & \SI{7.0}{\meter}  & \SI{5.9}{\meter}  & \SI{13.5}{\meter} && \SI{5.4}{\meter} & \SI{4.7}{\meter} & \SI{7.7}{\meter} && \SI{4.8}{\meter} & \SI{4.3}{\meter} & \SI{6.5}{\meter} \\
+		\bottomrule
 	\end{tabular}
 		\caption{Overall localization results in meter using the different impoverishment methods. For estimation we used the KDE-based method, as the errors compared to the weighted-average differ by only a few centimeter. The results are presented given the average positioning error $\bar{x}$, the standard deviation $\bar{\sigma}$ and the \SI{75}{\percent}-quantil of positioning errors $\tilde{x}_{75}$.}
 	\label{table:overall}
 \end{table}
-
 All walks, except for walk 1, suffer in some way from sample impoverishment. 
 We discuss the single results of table \ref{table:overall} starting with walk 0. 
 Here, the pedestrians started at the top most level, walking down to the lowest point of the building. 

tex_review/chapters/introduction.tex

@@ -23,12 +23,12 @@ We also use \del{a novel} \add{an} approach for finding an exact estimation of t
 
 Many historical buildings, especially bigger ones like castles, monasteries or churches, are built of massive stone walls and have annexes from different historical periods out of different construction materials.
 \del{This leads to problems} \add{This makes it more challenging to ensure good radio coverage of the entire building, especially} for technologies using received signal strengths indications (RSSI) from \docWIFI{} or Bluetooth.
-\add{For methods requiring environmental knowledge, like the wall-attenuation-factor model, the high signal attenuation between different rooms causes further problems.}
+\add{For methods requiring environmental knowledge, like signal strength prediction models, the high signal attenuation between different rooms causes further problems.}
 Many unknown quantities, like the walls definitive material or thickness, make it expensive to determine important parameters, \eg{} the signal's depletion over distance. 
 Additionally, \del{most wireless} \add{many of these} approaches are based on a line-of-sight assumption. 
 Thus, the performance will be even more limited due to the irregularly shaped spatial structure of such buildings. 
 Our approach tries to avoid those problems using an optimization scheme for Wi-Fi based on a \del{few} \add{set of} reference measurements.  
-We distribute a \del{small number} \add{set} of \del{simple} \add{small (\SI{2.8}{\centi\meter} x \SI{3.5}{\centi\meter})} and cheap \add{($\sim \SI{10}{\$}$)} \docWIFI{} beacons over the whole building \add{to ensure a reasonable coverage} and instead of measuring their position \add{and necessary parameters, we use our optimization scheme, initially presented in \cite{Ebner-17}}.
+We distribute a \del{small number} \add{set} of \del{simple} \add{small (\SI{2.8}{\centi\meter} x \SI{3.5}{\centi\meter})} and cheap \add{($\approx \SI{10}{\$}$)} \docWIFI{} beacons over the whole building \add{to ensure a reasonable coverage} and instead of measuring their position \add{and necessary parameters, we use our optimization scheme, initially presented in \cite{Ebner-17}}.
 
 \add{An optimization scheme is able to compensate for wrongly measured access point positions, inaccurate building plans or other knowledge necessary for the Wi-Fi component.
 }
@@ -44,7 +44,10 @@ However, this usually requires new cabling, e.g. an extra power over Ethernet co
 In contrast, the beacons can simply be plugged into already existing power outlets and due to their low price they can be distributed in large quantities, if necessary. 
 In the here presented scenario, the beacons do not establish a wireless network and thus serve only to provide signal strengths.}
 
-%To sum up, this work presents a smartphone-based localization system using.
+
+%Was brauchen wir für unser system?
+
+%Im Gegensatz zu vielen anderen Arbeiten 
 
 To sum up, \add{this work presents an updated version of the winning localization system of the smartphone-based competition at IPIN 2016 \cite{Ebner-15}, including the improvements and newly developed methods that have been made since then \cite{Ebner-16, Ebner-17, Fetzer-17, Bullmann-18}.
 This is the first time that all these previously acquired findings have been fully combined and applied simultaneously.
@@ -59,8 +62,14 @@ During the here presented update, the following novel contributions will be pres
 %Instead we use a simple optimization scheme based on reference measurements to estimate a corresponding \docWIFI{} model.
 
 The goal of this work is to propose a fast to deploy \del{and low-cost} localization solution, that provides reasonable results in a high variety of situations.
+\add{However, many state-of-the-art solutions are evaluating their systems within office or faculty buildings, offering a modern environment and well described infrastructure.} 
 Consequently, we believe that by utilizing our localization approach to such a challenging scenario, it is possible to prove those characteristics. 
-\add{Despite evaluating the novel contributions and the overall performance of the system, we have carried out additional experiments to determine the performance of our Wi-Fi optimization in such a complex scenario as well as a detailed comparison between KDE-based and weighted-average position estimation.}
+\add{To initially set up the system we only require a blueprint to create the floorplan, some Wi-Fi infrastructure, without any further information about access point positions or parameters, and a smartphone carried by the pedestrian to be localized. 
+The existing Wi-Fi infrastructure can consist of the aforementioned Wi-Fi beacons and / or already existing access points. 
+The combination of both technologies is feasible, depending on the scenario and building. 
+Nevertheless, the museum considered in this work has no Wi-Fi infrastructure at all, not even a single access point. 
+Thus, we distributed a set of \SI{42}{beacons} throughout the complete building by simply plugging them into available power outlets. 
+Despite evaluating the novel contributions and the overall performance of the system, we have carried out additional experiments to determine the performance of our Wi-Fi optimization in such a complex scenario as well as a detailed comparison between KDE-based and weighted-average position estimation.}
 
 %novel experiments to previous methods due to the complex scenario blah und blub.}
 %Finally, it should be mentioned that the here presented work is an highly updated version of the winner of the smartphone-based competition at IPIN 2016 \cite{Ebner-15}. 

tex_review/chapters/relatedwork.tex

@@ -54,7 +54,7 @@ for truly continuous transitions along the surface spanned by all triangles.
 %eval - wifi, fingerprinting
 The outcomes of the state evaluation process depend highly on the used sensors. 
 Most smartphone-based systems are using received signal strength indications (RSSI) given by \docWIFI{} or Bluetooth as a source for absolute positioning information. 
-At this, one can mainly distinguish between fingerprinting and signal-strength prediction model based solutions \cite{Ebner-17}.
+At this, one can mainly distinguish between fingerprinting and signal strength prediction model based solutions \cite{Ebner-17}.
 Indoor localization using \docWIFI{} fingerprints was first addressed by \cite{radar}. 
 During a one-time offline-phase, a multitude of reference measurements are conducted. 
 During the online-phase the pedestrian's location is then inferred by comparing those prior measurements against live readings.