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FrankE
2016-07-11 12:18:00 +02:00
parent ed2178e64c
commit a140bf2e38
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29
competition/tex/chapters/barometer.tex
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\subsection{Barometer} \subsection{Barometer}
used if available If available, the Smartphone's barometer is used to infer the likelyness of the current $z$-location%
relative positioning (z) %
relative to the first few measurements As ambient pressure readings are highly influenced by environmental conditions
also used to determine uncertainty like the weather, time-of-day and others \cite{Muralidharan14-BPS},
we use relative pressure instead of absolute ones.%
%
Due to noisy sensors, more than one reading is used to estimate this relative base.
The usual setup time of a navigation-system (route calculation, etc.)
is used to average all barometer readings during this timeframe.
This average $\overline{\mObsPressure}$ serves as relative base.
Furthermore, we estimate the sensor's uncertainty $\sigma_\text{baro}$ for later use within the evaluation step.%
%
During each transition from $\mStateVec_{t-1}$ to $\mStateVec_t$, we need a corresponding, relative
pressure prediction $\mStatePressure$ which is adjusted whenever a $z$-change happens within the transition.
%
% \begin{equation}
% \mState_{t}^{\mStatePressure} = \mState_{t-1}^{\mStatePressure} + \Delta z \cdot b
% ,\enskip
% \Delta z = \mState_{t-1}^{z} - \mState_{t}^z
% ,\enskip
% b \in \R
% \enspace .
% \label{eq:baroTransition}
% \end{equation}

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competition/tex/chapters/components.tex Normal file
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\section{Component Description}
Our indoor localisation solely uses the sensors provided by almost each commodity smartphone.
The readings of all those sensors are fused using recursive density estimation, directly on the phone:
\commentByFrank{state beschreiben: x, y, z, heading. oder machst du das schon weiter oben? dann kann vermutlicha uch die formel hier weg}
\begin{equation}
\arraycolsep=1.2pt
\begin{array}{ll}
&p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\
&\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})}_{\text{transition}}
\underbrace{p(\mStateVec_{t-1} \mid \mObsVec_{1:t-1})d\vec{q}_{t-1}}_{\text{recursion}} \enspace,
\end{array}
\label{eq:recursiveDensity}
\end{equation}
\docWIFI{} and (if available) \docIBeacon{}s serve as absolute positioning component. If the smartphone provides
a barometer, its measurements are used as an additional, relative verification for the current $z$-component
of the pedestrian's location.
The transition in \refeq{eq:recursiveDensity} is carried out using random walks on a graph, which is built offline, and uses
the building's floorplan. During the localisation process, the smartphone's IMU (accelerometer, gyroscope) is used to constrain the random walk
in both, distance and heading.
The recursive density estimation is implemented using a particle-filter.
\input{chapters/barometer.tex}
\input{chapters/wifi.tex}
\input{chapters/stepturn.tex}
\input{chapters/graph.tex}

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competition/tex/chapters/graph.tex
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\subsection{Transition} \subsection{Transition}
a graph based on the building's floorplan To enhance the quality of the proposal distribution, the transition step is
uses prior knowledge (floorplan + desired destination [if known]) based on a \SI{20}{\centimeter}-gridded graph $G = (V,E)$
uses random-walks to perform the transition with vertices $v_i \in V$ and undirected edges $e_{i,j} \in E$
uses imo (acc,gyro) for the random-walk (distance/direction) derived from the buildings floorplan. This ensures that only valid
calculated offline movements can be sampled from the previous state.%
\todo{wenn platz dann bild?}
The graph is built once and offline using the floorplan created by our editor.
Besides realistic stairwells, additional semantic information (e.g. doors)
can be included. Hereafter, the built graph is transmitted to the smartphone
and is used during the online phase.
If the pedestrian's destination is know beforehand, this information can
be included as prior knowledge into the transition step. A shortest-path
calculation imposes additional constraints to the transition by favouring
movements that approach the desired destination (pedestrian sticking to the shortest path)
over movements that depart from the destination.
To ensure that the calculated shortest path is realistic (resembled human walking paths)
each node within the graph contains a weight, denoting the likelyhood for being visited
by the pedestrian. Using this approach, nodes near to walls receive a lower likelyhood.
During the path-calculation this importance is used to artificially increase/decrease the
weight $\delta(\mEdgeAB)$ between two nodes. This ensures that the resulting path is
farther away from obstacles and looks much more realistic
\todo{wenn platz dann bild?}

12
competition/tex/chapters/introduction.tex
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\item grober ueberblick ueber die einzelnen komponenten und sensoren \item grober ueberblick ueber die einzelnen komponenten und sensoren
\item modulare uebersicht ueber das gesamte system. (denis bild + smoothing und prior) \item modulare uebersicht ueber das gesamte system. (denis bild + smoothing und prior)
\item particle filter mit formeluebersicht und was fusioniert wird \item particle filter mit formeluebersicht und was fusioniert wird
\begin{figure}[h!]
\centering%
\includegraphics[trim=99 0 0 0, clip, width=8.2cm]{editor1.png}%
\end{figure}
\end{itemize} \end{itemize}
\cite{ebner-15} \cite{ebner-15}
@@ -19,13 +23,9 @@ System setup is very easily and no fingerprinting is required.
\end{itemize} \end{itemize}
\section{Component Description}
Zu jeder Componente eine kurze Beschreibung welche die Grundfunktionen und den Wert innerhalb des Systems deutlich hervorhebt. Details werden dann referenziert.
\input{chapters/barometer.tex} \input{chapters/components.tex}
\input{chapters/wifi.tex}
\input{chapters/stepturn.tex}
\input{chapters/graph.tex}
\begin{itemize} \begin{itemize}
\item Fixed-lag smoother \item Fixed-lag smoother

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competition/tex/chapters/stepturn.tex
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\subsection{Step- and Turn-Detection} \subsection{Step- and Turn-Detection}
used for the transition step The smartphone's IMU is used to track the number of steps the pedestrian has made
uses the smartphone's imu (acc, gyro) (accelerometer) as well as the relative heading change (gyroscope) since the last transition
simple step detection using magnitudes \cite{ebner-15}.%
simple turn detection by integrating over the gyro %
feeds the transition To avoid potential sample impoverishment, which is induced when using the state transition as proposal distribution,
we use both values directly within the transition step to constrain the to-be-walked distance and direction for the
random walk.

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competition/tex/chapters/wifi.tex
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\subsection{\docWIFI} \subsection{\docWIFI}
absolute positioning using wifi (x,y,z) the smartphone's \docWIFI{} component provides an absolute location estimation $(x,y,z)^T$ by
no fingerprinting measuring the signal-strengths of nearby transmitters (\docAP{}) and comparing them with the signal-strengths
uses signal-strength prediction model that should be measurable. The latter are determined using a signal-strength prediction model.
position of access points must be known beforehand Thus, no fingerprinting is required. Solely the positions of the \docAP{}s must be known beforehand.%
only 2 parameters for all APs %
only vague position estimation Prediction uses the wall-attenuation-factor model \cite{ebner-15}
which needs just three parameters:
the senders transmission power, the attenuation based on the distance from the sender, and the attenuation by floors/ceilings.
To reduce the setup-time, the same values can be used for all transmitters at the expense of a worse location estimation / higher
uncertainties.
\subsection{\docIBeacon{}s} \subsection{\docIBeacon{}s}
optional If the building is equipped with \docIBeacon{}s, those may additionally be used as absolute location source.
same as wifi Like \docWIFI{}, measurements are compared with a model prediction based on the known transmitter position.
but only one param: as txp is broadcasted The transmission power of the \docIBeacon{} is transmitted by the beacon itself and does not need any estimation beforehand.

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competition/tex/make.sh
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PATH=$PATH:/mnt/data/texlive/bin/x86_64-linux/ PATH=$PATH:/mnt/data/texlive/bin/x86_64-linux/
latex bare_conf.tex pdflatex bare_conf.tex
bibtex bare_conf bibtex bare_conf
latex bare_conf.tex pdflatex bare_conf.tex
latex bare_conf.tex pdflatex bare_conf.tex
dvips bare_conf.dvi #dvips bare_conf.dvi
#ps2pdf14 bare_conf.ps
ps2pdf14 bare_conf.ps
atril bare_conf.pdf& atril bare_conf.pdf&