87 lines
6.4 KiB
TeX
87 lines
6.4 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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\commentByToni{"Sie sind ein Thema für sich". Glaub schon das man das so sagt. its own theme gibt es noch. find ich aber nicht so fresh}
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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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However, within this work, we use simple models, configured using a handful of parameters and address their inaccuracies by harnessing prior information like the pedestrian's desired destination.
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Therefore, we are not that interested in the different sensor representations but more in 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?} \commentByToni{ganz generell. deshalb nur connection line. line of sight ist ja mehr blickachse oder sichtlinie}
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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, Koeping14-ILU}.
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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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These disadvantages can be avoided by using spatial models like indoor graphs.
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Regarding modelling approaches, two main classes can be distinguished: symbolic and geometric spatial models \cite{Afyouni2012}.
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Especially geometric spatial models (coordinate-based approaches) are very popular, 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 diagram \cite{Liao2003}.
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It represents the topological skeleton of the building's floorplan as an irregular tessellation of space.
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This drastically removes degrees of freedom from the map, what results in a low complexity.
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In the work of \cite{Nurminen2014} a Voronoi diagram is used to approximate the human
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movement.
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It is assumed that the pedestrian can be anywhere on the topological links.
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The probabilities of changing to the next link are proportional to the total link lengths.
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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 adding those two-dimensional areas.
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The final graph is then created by sampling nodes in regular intervals across the links and filling up the open spaces in a tessellated manner.
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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 are still represented by just one topological link.
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The complexity is reduced but does not allow arbitrary movements and leads to suboptimal trajectories.
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Far more flexible and variable geometric spatial models are regular tessellated approaches like grid-based models.
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Those techniques are trivially implemented, but yet very powerful \cite{Afyouni2012}.
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Here, a square-shaped or hexagonal grid covers the entire map. Especially in the area of simultaneous localisation and mapping (SLAM), so-called occupancy-grid approaches are very popular \cite{elfes1989using, Thrun2003}.
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In an occupancy grid, a high probability is assigned to cells within accessible space, while cells occupied by obstacles or walls are less likely.
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Additionally, every grid cell is able to hold some context information about the environment (e.g. elevators or stairs) or the behaviour of a pedestrian at this particular position (e.g. jumping or running).
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A similar approach is presented in \cite{Li2010}, \cite{Ebner-15} and is also used within this work.
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By assuming that the floorplan is given beforehand, the occupied cells can be removed.
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The remaining cells are described by its centre and represent all free spaces in the indoor environment.
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A graph is defined by using the centres as nodes and connecting direct neighbours with edges.
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In order to enable floor changes, some approaches suggest to simply connect the nodes at staircases in a discrete manner \cite{}. However, as mentioned before changing the floor in a discrete does not resemble real-world conditions. Therefore, \cite{} presented a stepwise floor change based on a hexagonal gridded-graph. A similar approach is presented in the here presented approach for a square-shaped grid.
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All this allows a wide range of possibilities for modelling the pedestrian's movement, while only sampling valid locations.
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Computer Games
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Evacuation Route Planning
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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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