\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? 
	\subitem klarstellen was wir anders/besser machen
	\item graphen-basierte systeme
	\subitem probability graph / transition
	\item pathfinding for humans
	\subitem computerspiele machen das schon ewig. robotor auch.
	\subitem auf menschliches verhalten anpassen. gibt es viele theoritsche ansätze und simulationen aber in noch keinem system.
\end{itemize}