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toni first_draft

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Toni
2016-02-15 15:27:00 +01:00
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tex/chapters/introduction.tex
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@@ -41,6 +41,7 @@ Second, the heading for modelling the pedestrian's walking behaviour is calculat
and restricts the transition to perform only discrete \SI{45}{\degree} turns. While this is sufficient
for most cases, minor heading changes are often ignored and the posterior distribution (after walking)
is not smoothly spread.
\commentByToni{Und was machen wir dagegen?}
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.
@@ -50,14 +51,14 @@ Regarding a graph-based transition model, one could suggest to use the shortest
By incorporating this prior knowledge into the state transition step, a new state can be sampled in a more targeted manner.
However, for regular tessellated (grid) graphs, as used in \cite{Ebner-15}, this often leads to paths running very unnatural alongside walls.
Therefore, we present a novel approach that detects walls using the inverted graph (representing walls and obstacles) and a nearest-neighbour search.
\commentByFrank{hier kann man, wenn platz fehlt, vlt noch etwas details weglassen (inverted-graph, knn, etc)}
%\commentByFrank{hier kann man, wenn platz fehlt, vlt noch etwas details weglassen (inverted-graph, knn, etc)}
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.
Since areas near walls are 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:
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}
Therefore, a novel approach for detecting doors using again the inverted graph and a principal component analysis (PCA) \cite{Hotelling1933} is presented within this work.
%\commentByFrank{auch hier vlt das inverted erstmal noch weg lassen wegen platz}
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.