80 lines
5.5 KiB
TeX
80 lines
5.5 KiB
TeX
\section{Introduction}
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Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our everyday life.
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Whether driving a car, jogging or shopping in the streets, GNSS-based applications are making orientation easier,
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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, most of them do not work at all.
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That is because satellite signals are to weak to pass through obstacles like buildings' ceilings.
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Moreover, their accuracy is not sufficient for individual parking spaces or office rooms.
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Therefore, many different solutions for localizing a moving object within buildings have been developed in most recent years \cite{Ebner-15, Yang2015, Khaleghi2013, Fang09, Nurminen2014}.
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Especially the hard problem of pedestrian localization and navigation has lately attracted a lot of interest.
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Most modern indoor localisation systems primarily use smartphones for determining the position of a pedestrian.
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Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth
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are used for collecting the necessary data. Additionally, environmental knowledge is often incorporated by using
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floor maps. This combination of highly different sensor types is also known as sensor fusion.
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Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability
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distribution describing describing the pedestrian's possible whereabouts.
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This procedure can be separated into two probabilistic models:
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The transition model represents the dynamics of the pedestrian
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and predicts the next accessible locations,
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while the evaluation model estimates the probability for the position also corresponding to the recent sensor measurements.
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%Therefore, the most accurate position is represented by a peak of the probability distribution.
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In our previous work we were able to present such a localisation system based on all the above mentioned
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sensors including the phone's barometer \cite{Ebner-15}.
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In pedestrian navigation, the human movement underlies the characteristics of walking speed and walking direction.
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Additionally, environmental restrictions need to be considered as well, for example, walking through walls is in
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most cases impossible. Therefore, incorporating environmental knowledge is a necessary and gainful step.
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Like other systems, we are using a graph-based approach to sample only valid locations.
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The unique feature of our approach is the way in how we model the human movement.
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This is done by using random walks on a graph, which are based upon the heading of the
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pedestrian.
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Despite very good results
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However, the system presented in \cite{Ebner-15} suffers from two major drawbacks, we want to solve within this work.
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\commentByFrank{unser unique feature ist also, dass es nicht geht? :P so liest sich der absatz}
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Firstly, the transition model of our past \commentByFrank{previous?} approach uses discrete floors.
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\commentByFrank{floor-changes. die floors sind immernoch discrete}.
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Although the overall systems prevoides viable results, it does not resemble real-world floor changes.
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Especially the barometric sensor is affected due to its continuous pressure measurements.
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The discrete model restricts the barometer to exploit its full potential.
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\commentByFrank{komischer satz, schraenkt ein um das ganze potential zu nutzen? wie waers mit:
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prevents using the baromters full potential?}
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It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions.
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To address this problem we extended the graph by realistically shaped stairs, allowing a step-wise transition
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in the $z$-direction.
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Secondly, the heading for modeling the pedestrian's walking behaviour is calculated between two adjacent nodes.
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This restricts the transition to perform only \SI{45}{\degree} turns. In most scenarios this assumption performs
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well, since the... However, walking sharp turns and ... is not
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\commentByToni{Ich denke hier kann Frank E. noch bissle was schreiben, oder?}
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\commentByFrank{ja das werde ich noch anpassen, dass es stimmt und die probleme beschreibt}
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The problem of localization can be simplified by assuming a person navigation.
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\commentByFrank{???}
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Such applications are used to navigate a pedestrian to his desired destination.
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So, based on this assumption the starting point, which is the current position of the pedestrian,
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as well as the destination are known beforehand.
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\commentByFrank{die aktuelle post ist nicht vorher bekannt, jedenfalls verwenden wir es nicht so}
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Regarding a graph-based transition model, one could suggest to calculate the shortest path
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between start and destination. However, this often leads to paths running very unnatural alongside walls.
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\commentByFrank{zumindest bei unserem graphen layout. auf nem voronoi koennte es sogar besser sein}
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Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation
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and many other factors. Therefore, we present a novel method for pedestrian navigation by using \todo{XXX} methods
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to calculate a preferably realistic path:
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areas near a wall are less likely to be chosen for the path then a door or a small hallway. ... probability map/graph ...
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\commentByToni{Wissen ja noch nicht was wir hier genau nehmen, deswegen erstmal leer}
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to address the problem of walking on a corridor with higher probability ... a method for detecting doors and
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reducing the proabability of walking alongside walls will be presentend within this work...
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The work is structured as follows...
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