192 lines
12 KiB
TeX
192 lines
12 KiB
TeX
\section{Experiments}
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% introduction
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Evaluation took place within all floors (0 to 3) of the
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faculty building, each of which about \SI{77}{\meter} x \SI{55}{\meter} in size.
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%
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We conducted 4 distinct walks, for testing short distances, long distances, critical sections
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and ignoring the shortest-path suggested by the system.
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Due to an in-house exhibition during that time, many places were crowded and \docWIFI{} signals
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are attenuated more than usual.
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Each acquired path is backed by ground truth information to enable error calculation.
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This ground truth is measured by recording a timestamp at a marked spot on the walking route.
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During the walk, the pedestrian had to click a button on the smartphone application
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when passing a marker. Between two consecutive points, a constant movement speed is assumed.
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Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough to conduct
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error measurements. All walks were performed using a Motorola Nexus 6 and a Samsung Galaxy S5.
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As the Samsung Galaxy S5's \docWIFI{} can not be limited to the \SI{2.4}{\giga\hertz} band only,
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its scans take much longer than those of the Motorola Nexus 6:
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\SI{3500}{\milli\second} vs. \SI{600}{\milli\second}.
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Also, the Nexus' barometer sensor provides readings both more frequent and far more accurate than
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the Galaxy does. This results in a better localisation using the Nexus smartphone.
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Despite being fast enough to run in realtime on the smartphone itself, computation was done offline using
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the \mbox{CONDENSATION} particle filter with \SI{7500}{} particles as realization.
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The weighted arithmetic mean of the particles was used as state estimation.
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As mentioned earlier, the position of all \docAP{}s (about 5 per floor) is known beforehand.
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Due to legal terms, we are not allowed to depict their positions and therefore omit this information within the figures.
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Additionally, we used three \docIBeacon{}s for slight enhancements in some areas.
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The empirically chosen values for \docWIFI{} were
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$P_{0_{\text{wifi}}} = \SI{-46}{\dBm}, \mPLE_{\text{wifi}} = \SI{2.7}{}, \mWAF_{\text{wifi}} = \SI{8}{\dB}$,
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and $\mPLE_{\text{ib}} = \SI{1.5}{}$ for the \docIBeacon{}s, respectively.
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%
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Due to omitting a time-consuming calibration process for those values we expect the localisation process to
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perform generally worse compared to standard fingerprinting methods \cite{Ville09}. However, incorporating
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prior knowledge will often compensate for those poorly chosen system parameters.
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As uncertainties we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$, both growing with each measurement's age.
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While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters
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are determined automatically (see \ref{sec:sensBaro}). The step size for the transition was configured to be \SI{70}{\centimeter}
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with an allowed derivation of \SI{10}{\percent}. The heading deviation in
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\refeq{eq:transSimple}, \refeq{eq:transShortestPath} and \refeq{eq:transMultiPath} was \SI{25}{\degree}.
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As we start with a discrete uniform distribution for $\mStateVec_0$ (random position and heading), the first few estimations
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are omitted from the error calculation to allow the system to somewhat settle its initial state. Even though, the error
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during the following few seconds is expected to be much higher than the error when starting with a well known initial
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position and heading.
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The following evaluations will depict the improvements that the prior path knowledge is able to provide,
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even when other system parameters are badly chosen.
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Just adding importance-factors (described in \ref{sec:wallAvoidance} and \ref{sec:doorDetection})
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to the simple transition \refeq{eq:transSimple} addresses only minor local errors
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% like not sticking too close to walls. In most cases this lead only to slight improvements
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and is therefore not further evaluated.
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To examine the contribution our approach is able to provide, we will have a closer look
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at a long walk with many stairs, intentionally leaving the shortest path several times,
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named path 4 (see fig. \ref{fig:paths}).
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%
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% all paths we evaluated
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\begin{figure}
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\input{gfx/eval/paths}
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\caption{The four paths that were part of the evaluation.
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Starting positions are marked with black circles.
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For a better visualisation they were slightly shifted to avoid overlapping.}
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\label{fig:paths}
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\end{figure}
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% error development over time while walking along a path
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\begin{figure}
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\input{gfx/eval/error_timed_nexus}
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\caption{Development of the error while walking along
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%path 1 (upper) and
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path 4 (lower) using the Motorola Nexus 6.
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Path 4 shows increasing errors for our methods when leaving the shortest path (3) and when facing multimodalities between two
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staircases just before the destination (9).}
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\label{fig:errorTimedNexus}
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\end{figure}
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% detailed analysis of path 4
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\begin{figure}
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\input{gfx/eval/path_nexus_detail}
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\caption{Detailed path analysis depicting the individual segments of path 4. Their corresponding error contribution can
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be seen in fig. \ref{fig:errorTimedNexus}. Even though the shortest path suggested by the system is ignored multiple
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times ($3'$ and $3''$) our approach is still able to improve the overall localisation error.}
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\label{fig:nexusPathDetails}
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\end{figure}
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%
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\newcommand{\refSeg}[1]{$(#1)$}
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Fig. \ref{fig:errorTimedNexus} depicts the error for path 4 recorded with the Motorola Nexus 6.
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For a better understanding of the following discussion, the path was divided into $10$ individual segments.
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Remember that we start with a uniform distribution instead of a well known pedestrian location.
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Therefore, the first few estimations
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reside somewhere near the centre of the building and result in a very high error contribution
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as illustrated in fig. \ref{fig:nexusPathDetails} \refSeg{1}.
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%
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Even when removing those initial estimations from the error calculation, the next few seconds are still erroneous
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due to (intentionally) bad system parameters introduced in section \ref{sec:sensors}.
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Furthermore, as the pedestrian is not yet walking, our proposed method is also not yet able to address those errors.
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This can be seen at the red area in the upper left corner of fig. \ref{fig:nexusPathDetails} \refSeg{1} and within
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segment \refSeg{1} of fig. \ref{fig:errorTimedNexus}.
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%
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However, as soon as the pedestrian starts moving down the hallway \refSeg{2} the error is reduced dramatically.
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Adding prior knowledge centres the density in the middle of the floor, ensures that the heading is directed towards
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the shortest path and thus produces even better localisation results.
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%
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Directly hereafter, we ignore the shortest path \refSeg{3'} determined by the system and walk along \refSeg{3}
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instead. Of course, this leads to a temporally increasing error, as the system needs to detect this path change
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and takes some time to recover (see fig. \ref{fig:errorDistNexus} \refSeg{3}). The new path to the desired destination
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is \refSeg{3''} which is also ignored. Instead, we took a much longer route down the stairwell \refSeg{4}.
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After this change is detected by the system, prior knowledge is again able to reduce the error for segment \refSeg{5}.
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%
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Immediately hereafter follows a long, straight walk down the hallway. While the \docWIFI{} component pulls
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the pedestrian into the rooms on the right side, the actual walking route was located on the left side
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of the floor (see ground truth in fig. \ref{fig:nexusPathDetails} \refSeg{6}). While prior knowledge prevents
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the density from being dragged into the office-rooms, the estimated path is still located on the wrong side
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of the hallway. As both sides of the floor result in a route with almost the same length,
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just knowing the pedestrian's destination is not able to provide further improvements.
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Thus, a constant error of approximately the floor's width remains.
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This is clearly visible in fig. \ref{fig:nexusPathDetails} \refSeg{6}.
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%
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Due to the excellent barometer installed within the Nexus 6, changing the floor provides only small estimation
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errors in segment \refSeg{7}.
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It follows a critical area with high errors and multimodalities.
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Due to an in-house exhibition during the time of recording, we had to leave the ground truth by a few meters.
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Furthermore, the overcrowded areas lead to attenuated \docWIFI{} signals. Both reasons move the
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density into another stairwell (see fig. \ref{fig:nexusPathDetails}, red lines in the lower right).
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The resulting multimodality (two staircases possible at the same time) leads to a rising error
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\refSeg{8}, \refSeg{9}. At the end of the walk \refSeg{10} the system is able to recover, again.
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% overall error-distribution for nexus and galaxy
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\begin{figure}
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\input{gfx/eval/error_dist_nexus}
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\caption{Error distribution for all walks conducted with the Motorola Nexus 6. Our proposed methods
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clearly provide an enhancement for the overall localization process.}
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\label{fig:errorDistNexus}
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\end{figure}
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%\begin{figure}
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% \input{gfx/eval/error_dist_galaxy}
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% \caption{Nicht so markant beim galaxy, denke aber der platz reicht eh nicht, also einfach kurz erwaehnen}
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%\end{figure}
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The median error values for all other paths and the other smartphone are listed in table
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\ref{tbl:errNexus} and \ref{tbl:errGalaxy}. As can be seen, adding prior knowledge
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is able to improve the localisation for all examined situations, even when
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leaving the suggested path or when facing bad/slow sensor readings.
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% error values
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\begin{table}
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\centering
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\begin{tabular}{|l|c|c|c|c|}
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\hline
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& Path1 & Path2 & Path3 & Path4 \\\hline
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Simple (\refeq{eq:transSimple}) & \SI{6.68}{\meter} & \SI{5.25}{\meter} & \SI{4.32}{\meter} & \SI{3.84}{\meter} \\\hline
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Shortest (\refeq{eq:transShortestPath}) & \SI{2.72}{\meter} & \SI{2.98}{\meter} & \SI{2.48}{\meter} & \SI{3.06}{\meter} \\\hline
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Multipath (\refeq{eq:transMultiPath}) & \SI{2.62}{\meter} & \SI{2.14}{\meter} & \SI{2.46}{\meter} & \SI{2.75}{\meter} \\\hline
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\end{tabular}
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\caption{Median error for walks conducted with the Nexus 6.}
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\label{tbl:errNexus}
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\end{table}
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\begin{table}
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\centering
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\begin{tabular}{|l|c|c|c|c|}
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\hline
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& Path1 & Path2 & Path3 & Path4 \\\hline
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Simple (\refeq{eq:transSimple}) & \SI{10.03}{\meter} & \SI{7.65}{\meter} & \SI{6.03}{\meter} & \SI{7.54}{\meter} \\\hline
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Shortest (\refeq{eq:transShortestPath}) & \SI{ 5.86}{\meter} & \SI{4.14}{\meter} & \SI{5.14}{\meter} & \SI{5.20}{\meter} \\\hline
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Multipath (\refeq{eq:transMultiPath}) & \SI{ 6.35}{\meter} & \SI{4.21}{\meter} & \SI{5.03}{\meter} & \SI{6.79}{\meter} \\\hline
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\end{tabular}
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\caption{Median error for walks conducted with the Galaxy S5.}
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\label{tbl:errGalaxy}
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\end{table}
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%\begin{figure}
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% \includegraphics{gfx/eval/bergwerk_path1_galaxy}
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% \caption{Path 1 recorded with the Galaxy S5. Using prior knowledge improves the staircase (left) and the target area (right) where
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% both the barometer and \docWIFI{} provided bad readings.}
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% \label{fig:bergwerkPath1Galaxy}
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%\end{figure}
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%
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%\begin{figure}
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% \includegraphics{gfx/eval/bergwerk_path3_galaxy}
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% \caption{Path 3 recorded with the Galaxy S5. Even though both paths look similar, the version with prior knowledge ended
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% much closer to the real destination due to reduced delays.}
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% \label{fig:bergwerkPath3Galaxy}
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%\end{figure}
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%\commentByFrank{sensorausfall simulieren, z.b. in der mitte, oder auf einer treppe}
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%\commentByFrank{zwischendrin mal stehenbleiben und schauen ob auch das klappt}
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%\commentByFrank{zu grosser einfluss vom pfad ist also kein allheilmittel.. kann, wie beim treppenhaus, auch nach hinten los gehen}
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