in progress.. related work

tex/chapters/introduction.tex

@@ -2,7 +2,7 @@
 
 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. 
 
-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 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.
  %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}. 
 
@@ -15,6 +15,8 @@ Secondly, the heading for modeling the pedestrian's walking behaviour is calcula
 
 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 ... 
 \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...
+
 
 The work is structured as follows... 
 

tex/chapters/relatedwork.tex

@@ -1,27 +1,24 @@
 \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{} recently presentend an approach based on ... . 
+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}. 
 
-and \cite{} are combining pedestrian dead reckoning (PDR), Wi-Fi and  as information source. Here, \cite{} uses a fingerprinting approach for wi-fi in contrast to the ... model of \cite{}. 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{} or \cite{}. 
+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}. 
 
 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. 
 
-To avoid these disadvantages, from the outset, graph-based methods are becoming more and more popular. 
+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. 
 
-If the connection line between the new and the old position
+Nevertheless, most corridors 
 
-spatial models for indoor localization systems
-kann man unterscheiden: graph-based and random walk/non-graph based systeme. 
+...and walking into a rooms unwahrscheinlich.
+
+deshalb grided tessellation graph. blabalba for 2D environments. later for 3D .. 
+
+also hyprid version of both like presented in. they use blabal.. balab 
 
 
-random walk systeme
-
-graph systeme
-
-the work nearest to ours... 
-
-grided graph. blabalba for 2D environments. later for 3D ... 
 
+remove degrees of freedom from the map -> less particles
 
 \subsection{State Transition}