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changes toni, last half

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Toni
2016-02-29 13:35:50 +01:00
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tex/chapters/conclusion.tex
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@@ -2,7 +2,7 @@
We presented a novel approach to integrate prior navigation knowledge by using realistic human walking paths.
Based on a weighted graph, two different models for walking in a targeted and natural manner were introduced.
It could be shown that adding this additional knowledge causes an overall improvement of the localisation results, while maintaining flexibility for unexpected behaviour.
Furthermore, our approach is able to provide accurate and robust position estimations, even when (usually) necessary calibration processes are omitted.
Furthermore, our approach is able to provide accurate and robust position estimations, even when (usually) necessary calibration processes are avoided.
However, providing this calibration knowledge can further improve the results.
In order to reduce the effort of locating and calibrating \docAP{}s, a numerical optimization based on

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tex/chapters/experiments.tex
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% error development over time while walking along a path
\begin{figure}
\input{gfx/eval/error_timed_nexus}
\caption{Error development while walking along Path 4 using the Motorola Nexus 6.
When leaving the suggested route \refSeg{3}, the error of \textbf{shortest} path \refeq{eq:transShortestPath}
\caption{Error development while walking along Path 4 using the Nexus 6.
When leaving the suggested route in \refSeg{3}, the error of \textbf{shortest} path \refeq{eq:transShortestPath}
and \textbf{multi}path \refeq{eq:transMultiPath} increases.
The same issues arise when facing multimodalities between two staircases just before the destination \refSeg{9}.}
\label{fig:errorTimedNexus}
@@ -105,7 +105,7 @@
due to (intentionally) bad system parameters introduced in section \ref{sec:sensors}.
Furthermore, as the pedestrian is not yet walking, our proposed method is also not yet able to address those errors.
This can be seen at the red area in the upper left corner of fig. \ref{fig:nexusPathDetails} \refSeg{1} and within
segment \refSeg{1} of fig. \ref{fig:errorTimedNexus}.
\refSeg{1} of fig. \ref{fig:errorTimedNexus}.
%
Starting instead 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 the known target,
@@ -119,7 +119,7 @@
instead. Of course, this leads to a temporarily increasing error, as the system needs to detect this path change
and takes some time to recover (see fig. \ref{fig:errorTimedNexus} \refSeg{3}). The new path to the desired destination
is \refSeg{3''} which is also ignored. Instead, we took a much longer route down the stairwell \refSeg{4}.
After this change is detected by the system, prior knowledge is again able to reduce the error for segment \refSeg{5}.
After this change is detected by the system, prior knowledge is again able to reduce the error for \refSeg{5}.
%
Immediately hereafter follows a long, straight walk down the hallway. While the \docWIFI{} component pulls
the pedestrian into the rooms on the right side, the actual walking route was located on the left side
@@ -131,7 +131,7 @@
as seen in fig. \ref{fig:nexusPathDetails} \refSeg{6}.
%
Due to the excellent barometer installed within the Nexus 6, changing the floor provides only small estimation
errors in segment \refSeg{7}.
errors in \refSeg{7}.
It follows a critical area with high errors and multimodalities.
Due to an in-house exhibition during the time of recording, we had to leave the ground truth by a few meters.
Furthermore, the overcrowded areas lead to attenuated \docWIFI{} signals. This moves the