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fixed some gfx

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added some comments to the tex
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FrankE
2016-07-12 17:20:09 +02:00
parent f8d5449dbc
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8
competition/tex/chapters/introduction.tex
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@@ -5,8 +5,10 @@ For this, we have been awarded the best overall paper award at IPIN 2015 in Banf
Since then, we extended our approach by prior navigation knowledge using realistic human walking paths \cite{ebner-16} and smoothing methods \cite{fetzer-16}.
Additionally, a self-developed map editor allows for creating advanced 3D maps and realistically shaped stairs.
Compared to many other systems, we avoid any time-consuming fingerprinting and calibration processes and are able to start with a uniform distribution over the whole building.
\commentByFrank{= we do not need any prior information on the pedestrian's starting position}
All calculations are computed in real time on a commercial smartphone, in most of our examples this is the Motorola Nexus 6 or the Samsung Galaxy S5.
The system is implemented in C++ using the Qt framework and OpenCL.
The system is implemented in C++ using the Qt framework and OpenCL.
\commentByFrank{aktuell noch kein OpenCL leider}
\begin{figure}
\centering
@@ -16,7 +18,7 @@ The system is implemented in C++ using the Qt framework and OpenCL.
\end{figure}%
An overview of all involved components and the sensor fusion procedure can be seen in fig. \ref{fig:sysoverview}.
Here, the smartphone provides all necessary measurements and no additional device is needed.
Here, the smartphone provides all necessary measurements and no additional devices are needed.
The readings of all those sensors are fused using recursive density estimation, directly on the phone:
%
\begin{equation}
@@ -47,7 +49,7 @@ where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a seri
\end{equation}
%
where $\mRssiVec_\text{wifi}$ and $\mRssiVec_\text{ib}$ contain the measurements of all nearby \docAP{}s (\docAPshort{})
and \docIBeacon{}s, respectively. Both serve as absolute positioning component. $\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian.
and (if available) \docIBeacon{}s, respectively. Both serve as absolute positioning component. $\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian.
If the smartphone provides a barometer, $\mObsPressure$ is used as an additional, relative verification for the current $z$-component of the pedestrian's location.
%
The recursive density estimation of eq. \eqref{eq:recursiveDensity} is implemented using a particle-filter with the state transition as proposal density.