intro alpha
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@@ -15,7 +15,6 @@ floor maps. This combination of highly different sensor types is also known as s
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Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability
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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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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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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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The transition model represents the dynamics of the pedestrian
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and predicts the next accessible locations,
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and predicts the next accessible locations,
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@@ -25,55 +24,46 @@ In our previous work we were able to present such a localisation system based on
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sensors including the phone's barometer \cite{Ebner-15}.
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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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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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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.
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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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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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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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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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pedestrian.
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Despite very good results
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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.
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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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Firstly, the transition model of our previous approach uses discrete floor-changes.
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Although the overall systems provides viable results, it does not resemble real-world floor changes.
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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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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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The discrete model prevents the barometers 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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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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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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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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Secondly, the heading for modelling 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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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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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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\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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\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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To improve the complex problem of localising a person indoors, prior knowledge given by a pedestrian navigation can be used.
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\commentByFrank{???}
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Such applications are used to navigate a user to his desired destination.
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Such applications are used to navigate a pedestrian 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 starting point, which is the current position of the pedestrian,
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So, based on this assumption the destination is known beforehand and the starting point is the current estimated position of the pedestrian.
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as well as the destination are known beforehand.
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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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\commentByFrank{die aktuelle post ist nicht vorher bekannt, jedenfalls verwenden wir es nicht so}
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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 regular tessellated (grid) graphs, as used in \cite{Ebner-15}, this often leads to paths running very unnatural alongside walls.
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Therefore, we present a method that detects walls using the inverted graph (representing walls and obstacles) and a nearest-neighbour search.
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Regarding a graph-based transition model, one could suggest to calculate the shortest path
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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.
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between start and destination. However, this often leads to paths running very unnatural alongside walls.
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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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\commentByFrank{zumindest bei unserem graphen layout. auf nem voronoi koennte es sogar besser sein}
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Walking through a door has a lower probability than remaining on the corridor, since only a few nodes are representing it.
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Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation
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This can be tackled by making such areas more likely.
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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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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.
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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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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.
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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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%Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation and many other factors.
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@@ -2559,3 +2559,11 @@ year = {2014}
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@inproceedings{IPIN2015,
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@inproceedings{IPIN2015,
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title = {Multisensor 3D Indoor Localisation}
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title = {Multisensor 3D Indoor Localisation}
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}
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}
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@article{Hotelling1933,
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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.) },
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author = {Hotelling, H},
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title = {{Analysis of a complex of statistical variables into Principal Components. Jour. Educ. Psych., 24, 417-441, 498-520}},
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year = {1933}
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}
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