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stretched gfx (less height)

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removed some words for a better text-flow
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FrankE
2016-02-25 11:02:03 +01:00
parent 5701e709b2
commit 8f7a8d1ab1
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19
tex/chapters/experiments.tex
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@@ -6,15 +6,15 @@
Evaluation took place within all floors (0 to 3) of the
faculty building, each of which about \SI{77}{\meter} x \SI{55}{\meter} in size.
%
We conducted 4 distinct walks, for testing short distances, long distances, critical sections
We conducted 4 distinct walks, to test short distances, long distances, critical sections
and ignoring the shortest-path suggested by the system.
Due to an in-house exhibition during that time, many places were crowded and \docWIFI{} signals
are attenuated more than usual.
Each acquired path is backed by ground truth information to enable error calculation.
This ground truth is measured by recording a timestamp at a marked spot on the walking route.
During the walk, the pedestrian had to click a button on the smartphone application
are attenuated.
To enable error calculation, each acquired path is backed by ground truth information.
The ground truth is measured by recording a timestamp at marked spots on the walking route.
While walking, the pedestrian clicked a button on the smartphone application
when passing a marker. Between two consecutive points, a constant movement speed is assumed.
Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough to conduct
Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough for
error measurements. All walks were performed using a Motorola Nexus 6 and a Samsung Galaxy S5.
As the Samsung Galaxy S5's \docWIFI{} can not be limited to the \SI{2.4}{\giga\hertz} band only,
@@ -47,8 +47,8 @@
%\commentByFrank{$\mUsePath$ erklaert}
As we start with a discrete uniform distribution for $\mStateVec_0$ (random position and heading), the first few estimations
are omitted from the error calculation to allow the system to somewhat settle its initial state. Even though, the error
As we start with a uniform distribution (random position and heading) for $\mStateVec_0$, the first few estimations
are omitted from error calculations to allow the system to somewhat settle its initial state. Even though, the error
during the following few seconds is expected to be much higher than the error when starting with a well known initial
position and heading.
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@@ -111,7 +111,6 @@
Starting with both, known position and heading, reduced the error by about \SI{15}{\percent} when using prior knowledge and
by \SI{25}{\percent} when omitting prior knowledge. As prior knowledge directs the density towards a known target,
it is able to compensate unknown initial headings which explains the \SI{10}{\percent} difference.
\commentByFrank{bekannter startpunkt getestet und kurz beschrieben}
%
However, as soon as the pedestrian starts moving down the hallway \refSeg{2} the error is reduced dramatically.
Adding prior knowledge centres the density in the middle of the floor, ensures that the heading is directed towards
@@ -157,7 +156,7 @@
%\end{figure}
The median error values for all other paths and the other smartphone are listed in table
\ref{tbl:errNexus} and \ref{tbl:errGalaxy}. Furthermore, fig. \ref{fig:errorDistNexus}
\ref{tbl:errNexus}. Furthermore, fig. \ref{fig:errorDistNexus}
depicts the error development for several percentile values. As can be seen, adding prior
knowledge is able to improve the localisation for all examined situations, even when
leaving the suggested path or when facing bad/slow sensor readings.