doing related work... in progress

tex/chapters/introduction.tex

@@ -8,25 +8,13 @@ In our previous work we were able to present such a localisation system based on
 
 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. 
 
-Firstly, the transition model of our past approach uses discrete floors. Changing the floor on a discrete basis is like jumping down the staircase. This does not resemble real world floor changes and it could 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 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.
 
-Secondly, another drawback is the way in how the pedestrian's walking behaviour is modelled. At the moment the heading is only calculated between two adjacent nodes. That means, we are only able to perform \SI{45}{\degree} turns. \commentByToni{Ich denke hier kann Frank E. mehr zu schreiben. Bin mir nicht sicher wie ich das Problem gut schildern kann.} blumenverteilung, kurven laufen fällt schwer... bessers ziehen.
+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?} 
 
-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 choosen 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. 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}
 
-\begin{itemize}
-	\item Hinführen zum Thema 1/4 + Abstract (haben so wenig platz nur 8 seiten...)
-	\item "In unseren vergangenen Arbeiten konnten wir zeigen ... aber ..."
-	\item Probleme ganz konkret aufzählen und gleich die lösungen zu den problemen
-	  \subitem diskrete stockwerke, senkrecht 
-	  \subitem kurven laufen bereitet große probleme
-	  \subitem lokalisationsergebnisse sind zwar gut, aber vor allem an treppen instabil und räume werden schwer erkannt.
-	\item Was machen wir jetzt besser / anders. Hinführen zum Thema
-	    \subitem Kontinuierliche Stockwerke
-	    \subitem Bessers ziehen in der Transition 
-	    \subitem zusätzliche Gewichtung der Knoten anstatt nur annahme des geradeaus laufens mit geringer chance auf turns. 
-	    \subitem Annahme der Navigation
-	\item Aufbau der Arbeit (falls platz, haben nur 8 Seiten)
-\end{itemize}
+The work is structured as follows... 
 

tex/chapters/relatedwork.tex

@@ -1,5 +1,40 @@
 \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 ... . 
+
+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{}. 
+
+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. 
+
+If the connection line between the new and the old position
+
+spatial models for indoor localization systems
+kann man unterscheiden: graph-based and random walk/non-graph based systeme. 
+
+
+random walk systeme
+
+graph systeme
+
+the work nearest to ours... 
+
+grided graph. blabalba for 2D environments. later for 3D ... 
+
+
+\subsection{State Transition}
+
+In computer games like the sims or starcraft, intelligent npc movement is a key factor. hierbei geht es nicht nur um das umlaufen von hindernissen sondern auch um eine möglichst natürliche art der bewegung. 
+
+
+ansätze die dijkstra einfach zum navigieren nutzen.
+
+ansätze aus der robotic um einen roboter von a nach b zu schicken
+
+
+the idea of using navigational knowledge to simulate the human movement 
+
 \begin{itemize}
 	\item Allgemein indoor localizations systeme
 	\subitem was ist state of the art?