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tex/chapters/introduction.tex
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@@ -15,7 +15,6 @@ floor maps. This combination of highly different sensor types is also known as s
Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability
distribution describing describing the pedestrian's possible whereabouts.
This procedure can be separated into two probabilistic models:
The transition model represents the dynamics of the pedestrian
and predicts the next accessible locations,
@@ -25,55 +24,46 @@ In our previous work we were able to present such a localisation system based on
sensors including the phone's barometer \cite{Ebner-15}.
In pedestrian navigation, the human movement underlies the characteristics of walking speed and walking direction.
Additionally, environmental restrictions need to be considered as well, for example, walking through walls is in
most cases impossible. Therefore, incorporating environmental knowledge is a necessary and gainful step.
Additionally, environmental restrictions need to be considered as well, for example, walking through walls is in most cases 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 locations.
The unique feature of our approach is the way in how we model the human movement.
This is done by using random walks on a graph, which are based upon the heading of the
pedestrian.
Despite very good results
Despite very good results and a robust position estimation, the system presented in \cite{Ebner-15} suffers from two drawbacks, we want to solve within this work.
However, the system presented in \cite{Ebner-15} suffers from two major drawbacks, we want to solve within this work.
\commentByFrank{unser unique feature ist also, dass es nicht geht? :P so liest sich der absatz}
Firstly, the transition model of our past \commentByFrank{previous?} approach uses discrete floors.
\commentByFrank{floor-changes. die floors sind immernoch discrete}.
Although the overall systems prevoides viable results, it does not resemble real-world floor changes.
Firstly, 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 restricts the barometer to exploit its full potential.
\commentByFrank{komischer satz, schraenkt ein um das ganze potential zu nutzen? wie waers mit:
prevents using the baromters full potential?}
The discrete model prevents the barometers 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 realistically shaped stairs, allowing a step-wise transition
in the $z$-direction.
Secondly, the heading for modeling the pedestrian's walking behaviour is calculated between two adjacent nodes.
Secondly, the heading for modelling the pedestrian's walking behaviour is calculated between two adjacent nodes.
This restricts the transition to perform only \SI{45}{\degree} turns. In most scenarios this assumption performs
well, since the... However, walking sharp turns and ... is not
\commentByToni{Ich denke hier kann Frank E. noch bissle was schreiben, oder?}
\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}
The problem of localization can be simplified by assuming a person navigation.
\commentByFrank{???}
Such applications are used to navigate a pedestrian to his desired destination.
So, based on this assumption the starting point, which is the current position of the pedestrian,
as well as the destination are known beforehand.
\commentByFrank{die aktuelle post ist nicht vorher bekannt, jedenfalls verwenden wir es nicht so}
To improve the complex problem of localising a person indoors, prior knowledge given by a pedestrian navigation 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 current estimated position of the pedestrian.
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.
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 method that detects walls using the inverted graph (representing walls and obstacles) and a nearest-neighbour search.
Regarding a graph-based transition model, one could suggest to calculate the shortest path
between start and destination. However, this often leads to paths running very unnatural alongside walls.
\commentByFrank{zumindest bei unserem graphen layout. auf nem voronoi koennte es sogar besser sein}
Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation
and many other factors. Therefore, we present a novel method for pedestrian navigation by using \todo{XXX} methods
to calculate a preferably realistic path:
areas near a wall are less likely to be chosen for the path then a door or a small hallway. ... probability map/graph ...
\commentByToni{Wissen ja noch nicht was wir hier genau nehmen, deswegen erstmal leer}
to address the problem of walking on a corridor with higher probability ... a method for detecting doors and
reducing the proabability of walking alongside walls will be presentend within this work...
In order to express that 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:
Walking through a door has a lower probability than remaining on the corridor, since only a few nodes are representing it.
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.
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.
The work is structured as follows...
%Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation and many other factors.

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tex/egbib.bib
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@@ -2559,3 +2559,11 @@ year = {2014}
@inproceedings{IPIN2015,
title = {Multisensor 3D Indoor Localisation}
}
@article{Hotelling1933,
abstract = {The problem is stated in detail, a method of analysis is derived and its geometrical meaning shown, methods of solution are illustrated and certain derivative problems are discussed. (To be concluded in October issue.) },
author = {Hotelling, H},
title = {{Analysis of a complex of statistical variables into Principal Components. Jour. Educ. Psych., 24, 417-441, 498-520}},
year = {1933}
}