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senf zu experimente abgegeben

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Toni
2016-02-13 18:01:26 +01:00
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tex/chapters/introduction.tex
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@@ -11,38 +11,31 @@ Especially the hard problem of pedestrian localisation and navigation has lately
Most modern indoor localisation systems primarily use smartphones to determine the position of a pedestrian.
Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth
are used to collect the necessary data. Additionally, environmental knowledge is often incorporated e.g. by using
floor maps. This combination of highly different sensor types is also known as sensor fusion.
are used to collect the necessary data.
Additionally, environmental knowledge is often incorporated e.g. by using floormaps.
This combination of highly different sensor types is also known as sensor fusion.
Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability
distribution describing the pedestrian's possible whereabouts.
Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability distribution describing the pedestrian's possible whereabouts.
This procedure can be separated into two probabilistic models:
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
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
recent sensor measurements.
%Therefore, the most accurate position is represented by a peak of the probability distribution.
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}.
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}.
In pedestrian navigation, the human movement is subject to the characteristics of walking speed and -direction.
Additionally, environmental restrictions need to be considered as well, for example,
walking through walls is impossible.
Additionally, environmental restrictions need to be considered as well, for example, walking through walls is impossible.
Therefore, incorporating environmental knowledge is a necessary and gainful step.
Like other systems, we are using a graph-based approach to sample only valid movements.
The unique feature of our approach is the way how we model the human movement.
This is done by using random walks on a graph, which are based on the heading of the
pedestrian.
Despite very good results, the system presented in \cite{Ebner-15} suffers from two drawbacks,
we want to solve within this work.
This is done by using random walks on a graph, which are based on the heading of the pedestrian.
Despite very good results, the system presented in \cite{Ebner-15} suffers from two drawbacks, we want to solve within this work.
First, the transition model of our previous approach uses discrete floor-changes.
Although the overall systems provides viable results, it does not resemble real-world floor changes.
Especially the barometric sensor is affected due to its continuous pressure measurements.
The discrete model prevents the barometer's full potential.
It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions.
To address this problem we extended the graph by adding realistic stairs, allowing a step-wise transition
in the $z$-direction.
To address this problem we extended the graph by adding realistic stairs, allowing a step-wise transition in the $z$-direction.
Second, the heading for modelling the pedestrian's walking behaviour is calculated between two adjacent nodes.
This restricts the transition to perform only discrete \SI{45}{\degree} turns. In most scenarios this assumption performs
@@ -50,8 +43,7 @@ well, since the... However, walking sharp turns and ... is not
\commentByToni{Ich denke hier kann Frank E. noch bissle was schreiben, oder?}
\commentByFrank{ja das werde ich noch anpassen, dass es stimmt und die probleme beschreibt}
To improve the complex problem of localising a person indoors, prior knowledge given by a pedestrian navigation can be used.
\commentByFrank{klingt etwas komisch. -- given by a navigation system -- oder sowas in der art?}
To improve the complex problem of localising a person indoors, prior knowledge given by a navigation system can be used.
Such applications are used to navigate a user to his desired destination.
This limits the unpredictability of human movement to a certain degree.
So, based on this assumption, the destination is known beforehand and the starting point is the pedestrian's currently estimated position.
@@ -63,13 +55,11 @@ Therefore, we present a novel approach that detects walls using the inverted gra
In order to model areas near walls less likely to be chosen for walking, a probabilistic weight is assigned to every node of the graph.
This allows a variety of options for integrating additional knowledge about the environment and enables us to address another problem:
\commentByFrank{wie waers mit: entering or leaving rooms is very unlikely as only a few nodes (doors) allow doing so}
Walking through a door has a lower probability than remaining on the corridor, since only a few nodes are representing it.
Entering or leaving rooms is very unlikely as only a few nodes are representing doors and allow doing so.
This can be tackled by making such areas more likely.
Therefore, a novel approach for detecting doors using again the inverted graph and the principal component analysis (PCA) \cite{Hotelling1933} is presented within this work.
\commentByFrank{auch hier vlt das inverted erstmal noch weg lassen wegen platz}
\commentByFrank{wenn dir das weighted graph immer noch nicht gefaellt: -- weights attached the nodes -- oder sowas?}
Finally, it is now possible to calculate more natural and realistic paths using the weighted graph.
We introduce two different methods which make use of the given destination and thereby provide a targeted movement.
To the best of our knowledge, this approach is the first one that uses prior navigation knowledge to increase the localisation results.