78 lines
6.2 KiB
TeX
78 lines
6.2 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,
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GNSS-based applications simplify orientation,
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guide the way and even track our fitness achievements.
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%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 too weak to pass through obstacles like ceilings.
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However, satellite signals are too weak to pass through obstacles like ceilings and their accuracy is not sufficient for most indoor tasks.
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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 localising a moving object within buildings have been developed in
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the most recent years \cite{Ebner-15, Yang2015, Khaleghi2013, Fang09, Nurminen2014}.
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Especially the hard problem of pedestrian navigation has lately attracted a lot of interest.
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Most modern indoor localisation systems primarily use smartphones to determine 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 to collect the necessary data.
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Additionally, environmental knowledge is often incorporated e.g. by using floormaps.
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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 density 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, which represents the dynamics of the pedestrian and predicts his next accessible locations, and the evaluation model, which estimates the probability for the position also corresponding to
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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 sensors mentioned above, including the phone's barometer \cite{Ebner-15}.
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%In pedestrian navigation, the human movement is subject to the characteristics of walking speed and -direction.
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%Additionally, environmental restrictions need to be considered as well, for example, walking through walls is impossible.
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%Therefore, incorporating environmental knowledge is a necessary and gainful step.
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Incorporating environmental knowledge is a necessary and gainful step.
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For example walking through walls is impossible.
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Like other systems, we are using a graph-based approach to sample only valid movements.
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The unique feature of our approach is the way how human movement is modelled.
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This is done by using random walks on a graph, which are based on the heading of the pedestrian.
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Despite very good results, the system presented in \cite{Ebner-15} suffers from two drawbacks, we want to solve within this work.
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First, the transition model of our previous approach uses discrete floor-changes.
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Although the overall system provides viable results, it does not resemble real-world floor changes.
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Especially the barometer is affected due to its continuous pressure measurements.
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The discrete model prevents the barometer's 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 adding realistic stairs, allowing a step-wise transition in the $z$-direction.
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Second, the heading for modelling the pedestrian's walking behaviour is calculated between two adjacent nodes
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and restricts the transition to perform only discrete \SI{45}{\degree} turns. While this is sufficient
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for most cases, minor heading changes are often ignored and the posterior distribution (after walking)
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is not smoothly spread. The heading-change of our new approach is solely controlled by the smartphone's turn detection.
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During the random walk, matching edges are sampled according to their deviation from this change.
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To improve the complex problem of localising a person indoors, prior knowledge given by a navigation system can be used.
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Such applications are used to navigate a user to his desired destination.
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This limits the unpredictability of human movement to a certain degree.
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So, based on this assumption, the destination is known beforehand and the starting point is the user's currently estimated position.
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Regarding a graph-based transition model, one could suggest to use the shortest route between start and destination as the user's most-likely-to-walk path.
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By incorporating this prior knowledge into the state transition step, a new state can be sampled in a more targeted manner.
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However, for regularly tessellated (grid) graphs, as used in \cite{Ebner-15}, this would lead to unnatural paths e.g.
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directly adhering to walls.
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Therefore, we present a novel approach that detects walls using an inverted graph (representing walls and obstacles) and a nearest-neighbour search.
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%\commentByFrank{hier kann man, wenn platz fehlt, vlt noch etwas details weglassen (inverted-graph, knn, etc)}
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Since areas near walls are less likely to be chosen for walking, a probabilistic weight is assigned to every node of the graph.
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This allows a variety of options for integrating additional knowledge about the environment and enables us to address another problem:
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Entering or leaving rooms is very unlikely as only a few nodes are representing doors and allow doing so.
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This can be tackled by making such areas more likely.
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Therefore, a novel approach for detecting doors using the inverted graph is presented within this work.
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%\commentByFrank{auch hier vlt das inverted erstmal noch weg lassen wegen platz}
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Finally, it is now possible to calculate more natural and realistic paths using the weighted graph.
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We introduce two different methods which make use of the given destination and thereby provide a targeted movement.
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To the best of our knowledge, this approach is the first one that uses prior navigation knowledge to increase the localisation results.
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%Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation and many other factors.
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