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\section{Experiments}
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3 1/2 seiten sollten das schon werden. also eine ausführliche evaluation.
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As explained at the very beginning of this work, we wanted to explore the limits of the here presented localization system.
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By utilizing it to a 13th century historic building, we created a challenging scenario not only because of the various architectural factors, but also because of its function as a museum.
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During all experiments, the museum was open to the public and had a varying number of \SI{10}{} to \SI{50}{} visitors while recording.
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The \SI{2500}{\square\meter} building consists of \SI{6}{} different levels, which are grouped into 4 floors (see fig. \ref{fig:apfingerprint}).
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Thus, the ceiling height is not constant over one floor and varies between \SI{2.6}{\meter} to \SI{3.6}{\meter}.
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In the middle of the building is an outdoor area, which is only accessible from one side.
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While most of the exterior and ground level walls are made of massive stones, the floors above are half-timbered constructions.
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Due to different objects like exhibits, cabinets or signs not all positions within the building were walkable.
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For the sake of simplicity we did not incorporate such knowledge into the floorplan.
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Thus, the floorplan consists only of walls, ceilings, doors, windows and stairs.
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It was created using our 3D map editor software based on architectural drawings from the 1980s.
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The computation of the state estimation as well as the \docWifi{} optimization are done offline using an Intel Core i7-4702HQ CPU with a frequency of \SI{2.2}{GHz} running \SI{8}{cores} and \SI{16}{GB} main memory.
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However, similar to our previous, award-winning system, the setup is able to run completely on commercial smartphones as well as it uses C++ code \cite{torres2017smartphone}.
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%Sensor measurements are recorded using a simple mobile application that implements the standard Android SensorManager.
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The experiments are separated into three sections:
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At first, we discuss the performance of the novel transition model and compare it to a grid-based approach.
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In section \ref{sec:exp:opti} we have a look at \docWIFI{} optimization and how the real \docAPshort{} positions differ from it.
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Following, we conducted several test walks throughout the building to examine the estimation accuracy (in \SI{}{\meter}) of the localisation system.
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We try to resolve sample impoverishment with the here presented method and compare the different estimation methods as presented in section \ref{sec:estimation}.
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\subsection{Transition}
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To make a statement about the performance of our novel transition model presented within section \ref {}, we
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\todo{Unser liebes Treppensteigen. Vergleich altes und neues Bewegungsmodell.}
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\subsection{\docWIFI{} Optimization}
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\label{sec:exp:opti}
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%wie viele ap sind es insgesamt?
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The \docAPshort{} positions as well as the fingerprints used for optimization can be seen in fig. \ref{fig:apfingerprint}.
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As described in section \ref{sec:wifi} we used \SI{42}{} WEMOS D1 mini to provide a \docWIFI{} infrastructure throughout the building.
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The position of every installed beacon was measured using a laser scanner.
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This allows a comparison with the optimized \docAPshort{} positions.
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Within all Wi-Fi observations, we only consider the beacons, which are identified by their well-known MAC address.
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Other transmitters like smart TVs or smartphone hotspots are ignored as they might cause estimation errors.
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%wie fingerprints aufgenommen, wie viele ...
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\begin{figure}[bt]
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\centering
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\includegraphics[width=0.9\textwidth]{gfx/floorplanDummy.png}
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\caption{Floorplan Dummy}
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\label{fig:apfingerprint}
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\end{figure}
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%kurze beschreibung was wir jetzt alles testen wollen.
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%was kommt bei der optimierung raus. vergleichen mit ground truth. auch den fehler gegenüberstellen.
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%man sollte sehen das ohne optimierung gar nichts geht.
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\subsection{Location Estimation Error}
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\begin{figure}[ht]
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\centering
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\includegraphics[width=0.9\textwidth]{gfx/floorplanDummy.png}
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\caption{Floorplan Dummy}
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\label{fig:floorplan}
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\end{figure}
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%
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The 4 arranged walks can be seen in fig. \ref{fig:floorplan}.
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They were carried out be 4 different male testers using either a Samsung Note 2, Google Pixel One or Motorola Nexus 6 for recording the measurements.
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All in all, we recorded \SI{28}{} distinct measurement series, \SI{7}{} for each walk.
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A walk is indicated by a set of numbered markers, fixed to the ground.
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Small icons on those markers give the direction of the next marker and in some cases provide instructions to pause walking for a certain time.
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The intervals for pausing vary between \SI{10}{\second} to \SI{60}{\second}.
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The ground truth is then measured by recording a timestamp while passing a marker.
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For this, the tester clicks a button on the smartphone application.
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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 for error measurements.
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The approximation error is then calculated by comparing the interpolated ground truth position with the current estimation \cite{Fetzer-16}.
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%computation und monte carlo runs
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For each walk we deployed 50 runs using a varying size of particles.
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Instead of an initial position and heading, all walks start with a uniform distribution (random position and heading) as prior.
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%probleme mit impoverishment aufzeigen, wo bringt es was, was macht es kaputt etc pp
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%%estimation
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%wie in bulli paper.
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%letzer absatz nochmal gesamtergebniss des gesamten systems
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%was läuft noch schief? wo macht was probleme?
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\begin{itemize}
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\item Noch ein paar Dinge über das gebäude und das setup an sich
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