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added missing legend to gfx

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fixed some typos and refactored some sentences
This commit is contained in:
FrankE
2016-02-15 17:11:03 +01:00
parent ac542ba634
commit 54ab3d8dbe
9 changed files with 124 additions and 75 deletions
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20
tex/chapters/experiments.tex
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@@ -22,7 +22,7 @@
the Galaxy does. This results in a better localisation using the Nexus smartphone.
Despite being fast enough to run in realtime on the smartphone itself, computation was done offline using
the CONDENSATION particle filter with \SI{7500}{} particles as realization.
the \mbox{CONDENSATION} particle filter with \SI{7500}{} particles as realization.
The weighted arithmetic mean of the particles was used as state estimation.
As mentioned earlier, the position of all \docAP{}s (about 5 per floor) is known beforehand.
@@ -49,7 +49,7 @@
The following evaluations will depict the improvements that the prior path knowledge is able to provide,
even when other system parameters are badly chosen.
Just adding importance-factors described in \ref{sec:wallAvoidance} and \ref{sec:doorDetection}
Just adding importance-factors (described in \ref{sec:wallAvoidance} and \ref{sec:doorDetection})
to the simple transition \refeq{eq:transSimple} addresses only minor local errors
% like not sticking too close to walls. In most cases this lead only to slight improvements
and is therefore not further evaluated.
@@ -57,8 +57,6 @@
at a long walk with many stairs, intentionally leaving the shortest path several times,
named path 4 (see fig. \ref{fig:paths}).
%
% all paths we evaluated
\begin{figure}
\input{gfx/eval/paths}
@@ -67,8 +65,6 @@
For a better visualisation they were slightly shifted to avoid overlapping.}
\label{fig:paths}
\end{figure}
% error development over time while walking along a path
\begin{figure}
\input{gfx/eval/error_timed_nexus}
@@ -79,7 +75,6 @@
staircases just before the destination (9).}
\label{fig:errorTimedNexus}
\end{figure}
% detailed analysis of path 4
\begin{figure}
\input{gfx/eval/path_nexus_detail}
@@ -90,6 +85,7 @@
\end{figure}
%
\newcommand{\refSeg}[1]{$(#1)$}
Fig. \ref{fig:errorTimedNexus} depicts the error for path 4 recorded with the Motorola Nexus 6.
For a better understanding of the following discussion, the path was divided into $10$ individual segments.
Remember that we start with a uniform distribution instead of a well known pedestrian location.
@@ -145,15 +141,13 @@
%\end{figure}
The median error values for all other paths and the other smartphone are listed in table
\ref{tbl:errGalaxy} and \ref{tbl:errNexus}. As can be seen, adding prior knowledge
\ref{tbl:errNexus} and \ref{tbl:errGalaxy}. 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.
% error values
\begin{table}
\centering
\label{tbl:errNexus}
\caption{Median error for walks conducted with the Nexus 6.}
\begin{tabular}{|l|c|c|c|c|}
\hline
& Path1 & Path2 & Path3 & Path4 \\\hline
@@ -161,12 +155,12 @@
Shortest (\refeq{eq:transShortestPath}) & \SI{2.72}{\meter} & \SI{2.98}{\meter} & \SI{2.48}{\meter} & \SI{3.06}{\meter} \\\hline
Multipath (\refeq{eq:transMultiPath}) & \SI{2.62}{\meter} & \SI{2.14}{\meter} & \SI{2.46}{\meter} & \SI{2.75}{\meter} \\\hline
\end{tabular}
\caption{Median error for walks conducted with the Nexus 6.}
\label{tbl:errNexus}
\end{table}
\begin{table}
\centering
\label{tbl:errGalaxy}
\caption{Median error for walks conducted with the Galaxy S5.}
\begin{tabular}{|l|c|c|c|c|}
\hline
& Path1 & Path2 & Path3 & Path4 \\\hline
@@ -174,6 +168,8 @@
Shortest (\refeq{eq:transShortestPath}) & \SI{ 5.86}{\meter} & \SI{4.14}{\meter} & \SI{5.14}{\meter} & \SI{5.20}{\meter} \\\hline
Multipath (\refeq{eq:transMultiPath}) & \SI{ 6.35}{\meter} & \SI{4.21}{\meter} & \SI{5.03}{\meter} & \SI{6.79}{\meter} \\\hline
\end{tabular}
\caption{Median error for walks conducted with the Galaxy S5.}
\label{tbl:errGalaxy}
\end{table}
%\begin{figure}