88 lines
5.2 KiB
TeX
88 lines
5.2 KiB
TeX
\section{Related Work}
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Like mentioned before, most state-of-the-art systems use recursive state estimators like Kalman- and particle filters.
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They differ mainly by the sensors used, their probabilistic models and how the environmental information are incorporated.
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For example \cite{Li2015} recently presented an approach combining methods of pedestrian dead reckoning (PDR), Wi-Fi
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fingerprinting and magnetic matching using a Kalman filter. While providing good results, fingerprinting methods
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require an extensive offline calibration phase. Therefore, many other systems like \cite{Fang09} or \cite{Ebner-15}
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are using signal strength prediction models like the log-distance model or wall-attenuation-factor model.
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Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem, what makes the
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usage of Kalman filters problematic \cite{sarkka2013bayesian, Nurminen2014}.
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All this shows, that sensor models differ in many ways and are a subject in itself.
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\commentByFrank{sagt man das so? meinst du: haben ihr eigenes forschungsgebiet?}
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A good discussion on different sensor models can be found in \cite{Yang2015}, \cite{Gu2009} or \cite{Khaleghi2013}.
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\commentByFrank{However, within this work, we use simple models, configured using a handful of parameters
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and address their inaccuracies by harnassing prior information like the pedestrian's desired destination.}
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However, in regard of this work, we are not that interested in the different sensor representations but more in
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the state transition as well as incorporating environmental and navigational knowledge.
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A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position
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by adding an approximated covered distance to the current position. In most cases, a heading serves as
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walking direction. If the connection line \commentByFrank{graph? oder generell?: line-of-sight?}
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between the new and the old position intersects a wall, the probability for the new position is set to
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zero \cite{Woodman08-PLF, Blanchert09-IFF}.
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\commentByFrank{das hatte ich auch mit fast-0 auf der ipin2014. koennen wir auch noch citen}
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However, as \cite{Nurminen13-PSI} already stated, it "gives more probability to a short step".
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\commentByFrank{waende bevorzugen kurze schritte? wird das klar was hier gemeint ist?}
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An additional drawback of these approaches is that for every transition an intersection-test
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must be executed. This can result in a high computational complexity.
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\commentByFrank{ohja.. ipin2014 war brechend langsam}
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These disadvantages can be avoided, from the outset\commentByFrank{??}, by using spatial models
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like indoor graphs. Regarding modelling approaches, two main classes are inferred: \commentByFrank{richtiges wort hier?}
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symbolic and geometric spatial models \cite{Afyouni2012}.
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Especially geometric spatial models (coordinate-based approaches) are very popular,
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since they integrate metric properties to provide highly accurate location and distance information.
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One of the most common environmental representations in indoor localization literature is the Voronoi
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diagram \cite{Liao2003}. It represents the topological skeleton of the building's floorplan as an irregular
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tessellation of space. In the work of \cite{Nurminen2014} a Voronoi diagram is used to approximate the human
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movement. It is assumed that the pedestrian can be anywhere on the topological links.
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The choice probabilities \commentByFrank{??} of changing to the next link are proportional to the total link
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lengths. However, for highly accurate localisation and large-scale buildings, this network of one-dimensional
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curves is not suitable \cite{Afyouni2012}.
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Therefore, \cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram by
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adding those two-dimensional areas. \commentByFrank{was passsiert hier? wird nicht klar}
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The final graph is then created by sampling nodes in regular intervals from this structure.
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Similar to \cite{Ebner-15}, they provide a state transition model that selects an edge and a node
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from the graph according to a sampled distance and heading.
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Nevertheless, most corridors
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...and walking into a rooms unwahrscheinlich.
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deshalb grided tessellation graph. blabalba for 2D environments. later for 3D ..
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also hyprid version of both like presented in. they use blabal.. balab
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remove degrees of freedom from the map -> less particles
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\subsection{State Transition}
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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.
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ansätze die dijkstra einfach zum navigieren nutzen.
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ansätze aus der robotic um einen roboter von a nach b zu schicken
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the idea of using navigational knowledge to simulate the human movement
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\begin{itemize}
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\item Allgemein indoor localizations systeme
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\subitem was ist state of the art?
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\subitem klarstellen was wir anders/besser machen
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\item graphen-basierte systeme
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\subitem probability graph / transition
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\item pathfinding for humans
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\subitem computerspiele machen das schon ewig. robotor auch.
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\subitem auf menschliches verhalten anpassen. gibt es viele theoritsche ansätze und simulationen aber in noch keinem system.
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\end{itemize}
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