added tex comments

code/frank/OrientationObservation.h

@@ -0,0 +1,4 @@
+#ifndef ORIENTATIONOBSERVATION_H
+#define ORIENTATIONOBSERVATION_H
+
+#endif // ORIENTATIONOBSERVATION_H

code/frank/OrientationSensorReader.h

@@ -0,0 +1,4 @@
+#ifndef ORIENTATIONSENSORREADER_H
+#define ORIENTATIONSENSORREADER_H
+
+#endif // ORIENTATIONSENSORREADER_H

tex/bare_conf.tex

@@ -76,6 +76,7 @@
 \usepackage{eqparbox}
 
 \usepackage{epstopdf}
+\usepackage{ulem}
 
 
 % replacement for the SI package
@@ -182,6 +183,8 @@
 
 \input{chapters/system}
 
+\input{chapters/sensors}
+
 \input{chapters/floorplan}
 
 \input{chapters/grid}

tex/chapters/abstract.tex

@@ -1,5 +1,17 @@
 
 \begin{abstract}
-DUMMY ABSTRACT. We present an indoor localisation system that integrates different sensor modalities, namely Wi-Fi, barometer, iBeacons, step-detection and turn-detection for localisation of pedestrians within buildings over multiple floors. To model the pedestrian's movement, which is constrained by walls and other obstacles, we propose a state transition based upon random walks on graphs. This model also frees us from the burden of frequently updating the system. In addition we make use of barometer information to estimate the current floor. Furthermore, we present a statistical approach to avoid the incorporation of faulty heading information caused by changing the smartphone's position. 
-The evaluation of the system within a $\SI{77}{\meter}$ $\times$ $\SI{55}{\meter}$ sized building with 4 floors shows that high accuracy can be achieved while also keeping the update-rates low. 
+DUMMY ABSTRACT. We present an indoor localisation system that integrates different sensor modalities,
+namely Wi-Fi, barometer, iBeacons, step- and turn-detection for localisation of pedestrians within buildings
+over multiple floors. To model the pedestrian's movement, which is constrained by walls and other obstacles,
+we propose a state transition based upon random walks on graphs. \sout{This model also frees us from the burden of
+frequently updating the system.}
+In addition we make use of barometer information to estimate the current floor.
+\commentByFrank{entweder alle sensoren nennen, oder weglassen? sonst wirkt es nicht schluessig}ds
+Furthermore, we present a statistical approach to avoid the incorporation of faulty heading information caused
+by changing the smartphone's position. 
+\commentByFrank{ueber statistical reden wir nochma. einerseits ja, andererseits irgendwie nein.}
+The evaluation of the system within a $\SI{77}{\meter}$ $\times$ $\SI{55}{\meter}$ sized building with 4 floors
+shows that high accuracy can be achieved while also keeping the update-rates low. 
+\commentByFrank{We will show that incorporating prior knowledge, such as the pedestrian's desired destination,
+improves the overall localisation process and prevents various error-conditions.}
 \end{abstract}

tex/chapters/introduction.tex

@@ -1,21 +1,78 @@
 \section{Introduction}
 
-Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our lives. Whether driving a car, jogging or shopping in the streets, GNSS-based applications are making orientation easier, point the way and even track our fitness achievements. But as soon as we drive into an underground car park or visit a shopping mall, they perform poorly. That is because satellite signals are to weak to pass through obstacles like buildings' walls. Moreover, their accuracy is not sufficient for individual parking spaces or rooms. Therefore, many different solutions for localizing a moving object within buildings have been developed in recent years \cite{}. Especially the hard problem of pedestrian localization and navigation has lately attracted a lot of interest. 
+Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our lives.
+\commentByFrank{everyday life?}
+Whether driving a car, jogging or shopping in the streets, GNSS-based applications are making orientation easier,
+point the way and even track our fitness achievements. But as soon as we drive into an underground car park or visit a shopping mall,
+they perform poorly \commentByFrank{most of them do not work at all?}.
+That is because satellite signals are to weak to pass through obstacles like buildings' walls \commentByFrank{floors. kommen ja von oben}.
+Moreover, their accuracy is not sufficient for individual parking spaces or rooms \commentByFrank{rooms? im parkhaus?}.
+Therefore, many different solutions for localizing a moving object within buildings have been developed in \commentByFrank{in the most recent?} recent years \cite{}.
+Especially the hard problem of pedestrian localization and navigation has lately attracted a lot of interest. 
 
-Most modern indoor localisation systems primarily use smartphones for determining the position of a pedestrian. Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth are used for collecting the necessary data. Additionally, environmental knowledge is often incorporated by using floor maps. This combination of highly different sensor types is also known as sensor fusion. Here, probabilistic methods like particle filters or Kalman filters or often used to approximate a probability distribution describing the uncertainties of the system. This procedure can be separated into two probabilistic models: The transition model represents the dynamics of the system and predicts the next accessible locations, while the evaluation model estimates a probability that the position also corresponds to the current sensor measurement.
+Most modern indoor localisation systems primarily use smartphones for determining the position of a pedestrian.
+Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth
+are used for collecting the necessary data. Additionally, environmental knowledge is often incorporated by using
+floor maps. This combination of highly different sensor types is also known as sensor fusion.
+
+Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability
+distribution describing the uncertainties of the system.
+\commentByFrank{interessieren uns die unsicherheiten, oder eher die wahrscheinlichkeit des hidden sate?
+describing the pedestrian's possible whereabouts?}
+
+This procedure can be separated into two probabilistic models:
+The transition model represents the dynamics of the system \commentByFrank{eher pedestrian? den modellieren wir ja}
+and predicts the next accessible locations,
+while the evaluation model estimates the probability for the position also corresponding to the recent sensor measurements.
  %Therefore, the most accurate position is represented by a peak of the probability distribution. 
-In our previous work we were able to present such a localisation system based on all the above mentioned sensors including the phone's barometer \cite{Ebner-15}. 
+In our previous work we were able to present such a localisation system based on all the above mentioned
+sensors including the phone's barometer \cite{Ebner-15}.
+\commentByFrank{das baro ist schon wieder einzeln aufgezaehlt?}
 
-In pedestrian navigation, the human movement underlies the characteristics of walking speed and walking direction. Additionally, environmental restrictions need to be considered as well, for example, walking through walls is in most cases impossible. Therefore, incorporating environmental knowledge is a necessary and gainful step. Like other systems, we are using a graph-based approach for this. The main advantage of such an approach is that the graph only samples valid locations. The unique feature of our approach is the way in how we model the human movement. This is done by using random walks on graphs, which are based upon the heading of the pedestrian. However, the system presented in \cite{Ebner-15} suffers from two major drawbacks, we want to solve within this work. 
+In pedestrian navigation, the human movement underlies the characteristics of walking speed and walking direction.
+Additionally, environmental restrictions need to be considered as well, for example, walking through walls is in
+most cases impossible. Therefore, incorporating environmental knowledge is a necessary and gainful step.
+Like other systems, we are using a graph-based approach to sample only valid locations.
+The unique feature of our approach is the way in how we model the human movement.
+This is done by using random walks on graphs \commentByFrank{the graph?}, which are based upon the heading of the
+pedestrian. However, the system presented in \cite{Ebner-15} suffers from two major drawbacks, we want to solve within this work.
+\commentByFrank{unser unique feature ist also, dass es nicht geht? :P so liest sich der absatz}
 
-Firstly, the transition model of our past approach uses discrete floors. Although the approach performs good, it does not resemble real-world floor changes. Especially the barometric sensor is affected due to its continuous pressure measurements. The discrete models restricts the barometer to exploit its full potential. It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions. To address this problem we extended the graph by realistically shaped stairs, allowing a step-wise transition in the $z$-direction.
+Firstly, the transition model of our past \commentByFrank{previous?} approach uses discrete floors.
+\commentByFrank{floor-changes. die floors sind immernoch discrete}.
+Although the overall systems prevoides viable results, it does not resemble real-world floor changes.
+Especially the barometric sensor is affected due to its continuous pressure measurements.
+The discrete model restricts the barometer to exploit its full potential.
+\commentByFrank{komischer satz, schraenkt ein um das ganze potential zu nutzen? wie waers mit:
+prevents using the baromters full potential?}
 
-Secondly, the heading for modeling the pedestrian's walking behaviour is calculated between two adjacent nodes. This restricts the transition to perform only \SI{45}{\degree} turns. In most scenarios this assumption performs well, since the... However, walking sharp turns and ... is not 
+It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions.
+To address this problem we extended the graph by realistically shaped stairs, allowing a step-wise transition
+in the $z$-direction.
+
+Secondly, the heading for modeling the pedestrian's walking behaviour is calculated between two adjacent nodes.
+This restricts the transition to perform only \SI{45}{\degree} turns. In most scenarios this assumption performs
+well, since the... However, walking sharp turns and ... is not 
  \commentByToni{Ich denke hier kann Frank E. noch bissle was schreiben, oder?} 
+\commentByFrank{ja das werde ich noch anpassen, dass es stimmt und die probleme beschreibt}
 
-The problem of localization can be simplified by assuming a person navigation. Such applications are used to navigate a pedestrian to a given target destination. So, based on this assumption the starting point, which is the current position of the pedestrian, as well as the destination are known beforehand. Regarding a graph-based transition model, one could suggest to calculate the shortest path between start and destination. However, this often leads to paths running very unnatural alongside walls. Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation and many other factors. Therefore, we present a novel method for pedestrian navigation by using XXX methods to achieve a preferably realistic path, areas near a wall are less likely to be chosen for the path then a door or a small hallway. ... probability map/graph ... 
+The problem of localization can be simplified by assuming a person navigation.
+\commentByFrank{???}
+Such applications are used to navigate a pedestrian to his desired destination.
+So, based on this assumption the starting point, which is the current position of the pedestrian,
+as well as the destination are known beforehand.
+\commentByFrank{die aktuelle post ist nicht vorher bekannt, jedenfalls verwenden wir es nicht so}
+
+Regarding a graph-based transition model, one could suggest to calculate the shortest path
+between start and destination. However, this often leads to paths running very unnatural alongside walls.
+\commentByFrank{zumindest bei unserem graphen layout. auf nem voronoi koennte es sogar besser sein}
+Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation
+and many other factors. Therefore, we present a novel method for pedestrian navigation by using \todo{XXX} methods
+to calculate a preferably realistic path:
+areas near a wall are less likely to be chosen for the path then a door or a small hallway. ... probability map/graph ... 
 \commentByToni{Wissen ja noch nicht was wir hier genau nehmen, deswegen erstmal leer}
-to address the problem of walking on a corridor with higher probability ... a method for detecting doors and reducing the proabability of walking alongside walls will be presentend within this work...
+to address the problem of walking on a corridor with higher probability ... a method for detecting doors and
+reducing the proabability of walking alongside walls will be presentend within this work...
 
 
 The work is structured as follows... 

tex/chapters/relatedwork.tex

@@ -1,12 +1,53 @@
 \section{Related Work}
 
-Like mentioned before, most state-of-the-art systems use recursive state estimators like Kalman filters and particle filters. They differ mainly by the sensors used, their probabilistic models and how the environmental information is incorporated. For example \cite{Li2015} recently presented an approach combining methods of pedestrian dead reckoning (PDR), Wi-Fi fingerprinting and magnetic matching using a Kalman filter. Regardless of the good results, fingerprinting methods require an extensive offline calibration phase. Therefore, many other systems like \cite{Fang09} or \cite{Ebner-15} are using signal strength prediction models like the log-distance model or wall-attenuation-factor model. Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem, what makes the usage of Kalman filters problematic \cite{sarkka2013bayesian, Nurminen2014}. 
+Like mentioned before, most state-of-the-art systems use recursive state estimators like Kalman- and particle filters.
+They differ mainly by the sensors used, their probabilistic models and how the environmental information are incorporated.
+For example \cite{Li2015} recently presented an approach combining methods of pedestrian dead reckoning (PDR), Wi-Fi
+fingerprinting and magnetic matching using a Kalman filter. While providing good results, fingerprinting methods
+require an extensive offline calibration phase. Therefore, many other systems like \cite{Fang09} or \cite{Ebner-15}
+are using signal strength prediction models like the log-distance model or wall-attenuation-factor model.
+Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem, what makes the
+usage of Kalman filters problematic \cite{sarkka2013bayesian, Nurminen2014}.
 
-All this shows, that sensor models differ in many ways and are a subject in itself. However, in regard of this work, we are not that interested in the different sensor representations but more in the state transition as well as incorporating environmental and navigational knowledge. A good discussion on different sensor models can be found in \cite{Yang2015}, \cite{Gu2009} or \cite{Khaleghi2013}. 
+All this shows, that sensor models differ in many ways and are a subject in itself.
+\commentByFrank{sagt man das so? meinst du: haben ihr eigenes forschungsgebiet?}
+A good discussion on different sensor models can be found in \cite{Yang2015}, \cite{Gu2009} or \cite{Khaleghi2013}. 
 
-A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position by adding an approximated covered distance to the current position. In most cases, the heading serves as walking direction. If the connection line between the new and the old position intersects a wall, the probability for the new position is set to zero \cite{Woodman08-PLF, Blanchert09-IFF}. However, as \cite{Nurminen13-PSI} already stated, it "gives more probability to a short step". An additional drawback of these approaches is that for every transition an intersection-test must be executed. This can result in a high computational complexity. 
+\commentByFrank{However, within this work, we use simple models, configured using a handful of parameters
+and address their inaccuracies by harnassing prior information like the pedestrian's desired destination.}
+However, in regard of this work, we are not that interested in the different sensor representations but more in
+the state transition as well as incorporating environmental and navigational knowledge. 
 
-These disadvantages can be avoided, from the outset, by using spatial models like indoor graphs. Regarding modelling approaches, two main classes are inferred: symbolic and geometric spatial models \cite{Afyouni2012}. Especially geometric spatial models (coordinate-based approaches) are very popular, since they integrate metric properties to provide highly accurate location and distance information. One of the most common environmental representations in indoor localization literature is the Voronoi diagram \cite{Liao2003}. It represents the topological skeleton of the building's floorplan as an irregular tessellation of space. In the work of \cite{Nurminen2014} a Voronoi diagram is used to approximate the human movement. It is assumed that the pedestrian can be anywhere on the topological links. The choice probabilities of changing to the next link are proportional to the total link lengths. However, for highly accurate localisation and large-scale buildings, this network of one-dimensional curves is not suitable \cite{Afyouni2012}. Therefore, \cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram by adding those two-dimensional areas. The final graph is then created by sampling nodes in regular intervals from this structure. Similar to \cite{Ebner-15}, they provide a state transition model that selects and edge and a node from the graph according to a sampled distance and heading. 
+A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position
+by adding an approximated covered distance to the current position. In most cases, a heading serves as
+walking direction. If the connection line \commentByFrank{graph? oder generell?: line-of-sight?}
+between the new and the old position intersects a wall, the probability for the new position is set to
+zero \cite{Woodman08-PLF, Blanchert09-IFF}.
+\commentByFrank{das hatte ich auch mit fast-0 auf der ipin2014. koennen wir auch noch citen}
+
+However, as \cite{Nurminen13-PSI} already stated, it "gives more probability to a short step".
+\commentByFrank{waende bevorzugen kurze schritte? wird das klar was hier gemeint ist?}
+An additional drawback of these approaches is that for every transition an intersection-test
+must be executed. This can result in a high computational complexity.
+\commentByFrank{ohja.. ipin2014 war brechend langsam}
+
+These disadvantages can be avoided, from the outset\commentByFrank{??}, by using spatial models
+like indoor graphs. Regarding modelling approaches, two main classes are inferred: \commentByFrank{richtiges wort hier?}
+symbolic and geometric spatial models \cite{Afyouni2012}.
+Especially geometric spatial models (coordinate-based approaches) are very popular,
+since they integrate metric properties to provide highly accurate location and distance information.
+One of the most common environmental representations in indoor localization literature is the Voronoi
+diagram \cite{Liao2003}. It represents the topological skeleton of the building's floorplan as an irregular
+tessellation of space. In the work of \cite{Nurminen2014} a Voronoi diagram is used to approximate the human
+movement. It is assumed that the pedestrian can be anywhere on the topological links.
+The choice probabilities \commentByFrank{??} of changing to the next link are proportional to the total link
+lengths. However, for highly accurate localisation and large-scale buildings, this network of one-dimensional
+curves is not suitable \cite{Afyouni2012}.
+Therefore, \cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram by
+adding those two-dimensional areas. \commentByFrank{was passsiert hier? wird nicht klar}
+The final graph is then created by sampling nodes in regular intervals from this structure.
+Similar to \cite{Ebner-15}, they provide a state transition model that selects an edge and a node
+from the graph according to a sampled distance and heading. 
 
 Nevertheless, most corridors 
 

tex/chapters/sensors.tex

@@ -0,0 +1,14 @@
+\section{Sensors}
+
+	\subsection{Barometer}
+		As stated by \cite{ipin2015} \todo{and the other paper directly}, ambient pressure readings are highly influenced
+		by environmental conditions like the weather, time-of-day and others. Thus, relative pressure readings are
+		preferred over absolute ones. However, due to noisy sensors \todo{cite oder grafik? je nach platz}, one
+		single reading is not enough as a relative base. Harnessing the usual setup time of a navigation-system (
+		route calculation, user checking the route) we use the average of all barometer readings during this
+		timeframe as realtive base $\overline{\mPressure}$.
+		
+		
+	\subsection{Wi-Fi \& iBeacons}
+	
+	\subsection{Step- \& Turn-Detection}

tex/egbib.bib

@@ -2555,3 +2555,7 @@ title = {{Graph-based Data Fusion of Pedometer and WiFi Measurements for Mobile
 year = {2014}
 }
 
+
+@inproceedings{IPIN2015,
+title = {Multisensor 3D Indoor Localisation}
+}

tex/misc/functions.tex

@@ -28,6 +28,8 @@
 
 \newcommand{\mMovingAvgWithSize}[1]{\ensuremath{\text{avg}_{#1}}}
 
+\newcommand{\mPressure}{\rho}							% symbol for pressure readings
+
 %\newcommand{\docIBeacon}{iBeacon}
 
 % for equation references