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added missing legend to gfx

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fixed some typos and refactored some sentences
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FrankE
2016-02-15 17:11:03 +01:00
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tex/chapters/abstract.tex
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\begin{abstract}
Navigating to a desired destination is a key aspect of indoor localisation. Up to this point many different systems using present or past information for estimating the pedestrian's position were presented.
Our work proposes a novel approach that incorporates prior navigation knowledge by using realistic human walking paths.
In order to create such paths, we present a method that assigns an importance factor to every node of a regular tessellated graph by avoiding walls and detecting doors.
In order to create such paths, we present a method that assigns an importance-factor to every node of a regularly tessellated graph by avoiding walls and detecting doors.
The human movement is then modelled by moving along adjacent nodes into the most proper walking-direction.
To be able of going into the 3rd dimension, realistically shaped stairs for step-wise floor changes are used.
The position is estimated over multiple floors integrating different sensor modalities, namely Wi-Fi, iBeacons, barometer, step-detection and turn-detection.
To enable 3D localisation, realistically shaped stairs for step-wise floor changes are used.
The position is estimated over multiple floors integrating different sensor modalities, namely Wi-Fi, iBeacons, barometer, step- and turn-detection.
The system was tested by omitting any time-consuming calibration process and starts with a uniform distribution instead of a well known pedestrian location.
The evaluation shows that adding prior knowledge is able to improve the localisation, even for unpredictable behaviour, faulty measurements and for poorly chosen system parameters.
The evaluation shows that adding prior knowledge is able to improve the localisation, even under unpredictable behaviour, faulty measurements and poorly chosen system parameters.
\end{abstract}