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25
competition/tex/chapters/components.tex
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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}

55
competition/tex/chapters/introduction.tex
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The navigation system is based on our previous works, primarily on the approach presented in \cite{ebner-15}.
For this, we have been awarded the best overall paper award at IPIN 2015 in Banff, Canada.
Since then, we extended our approach by prior navigation knowledge using realistic human walking paths \cite{} and smoothing methods \cite{}.
Since then, we extended our approach by prior navigation knowledge using realistic human walking paths \cite{ebner-16} and smoothing methods \cite{fetzer-16}.
Additionally, a self-developed map editor allows for creating advanced 3D maps and realistically shaped stairs.
Compared to many other systems, we avoid any time-consuming fingerprinting and calibration processes and are able to start with a uniform distribution over the whole building.
All calculations are computed in real time on a commercial smartphone, in most of our examples this is the Motorola Nexus 6.
All calculations are computed in real time on a commercial smartphone, in most of our examples this is the Motorola Nexus 6 or the Samsung Galaxy S5.
The system is implemented in C++ using the Qt framework and OpenCL.
An overview of all involved components and the sensor fusion procedure can be seen in fig. \ref{}.
Here, the smartphone provides all necessary measurements and no additional device is needed.
The readings of all those sensors are fused using recursive density estimation, directly on the phone:
%
\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}
%
where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a series of observations up to time $t$.
The hidden state $\mStateVec$ is given by
\begin{equation}
\mStateVec = (x, y, z, \mStateHeading, \mStatePressure),\enskip
x, y, z, \mStateHeading, \mStatePressure \in \R \enspace,
\end{equation}
%
where $x, y, z$ represent the position in 3D space, $\mStateHeading$ the user's heading and $\mStatePressure$ the relative atmospheric pressure prediction in hectopascal (hPa).
The recursive part of the density estimation contains all information up to time $t-1$.
Furthermore, the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ models the pedestrian's movement and is carried out using random walks on a graph, which is built offline, and uses the building's floorplan \cite{ebner-16}.
Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
%
\begin{equation}
\mObsVec = (\mRssiVec_\text{wifi}, \mRssiVec_\text{ib}, \mObsHeading, \mObsSteps, \mObsPressure) \enspace,
\end{equation}
%
where $\mRssiVec_\text{wifi}$ and $\mRssiVec_\text{ib}$ contain the measurements of all nearby \docAP{}s (\docAPshort{})
and \docIBeacon{}s, respectively. Both serve as absolute positioning component. $\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian.
If the smartphone provides a barometer, $\mObsPressure$ is used as an additional, relative verification for the current $z$-component of the pedestrian's location.
%
The recursive density estimation of eq. \eqref{eq:recursiveDensity} is implemented using a particle-filter with the state transition as proposal density.
This ensures valid position estimations even if a sensor is defect or is not provided by the smartphone itself.
This ensures sensor is defect or is not provided by the smartphone itself
\begin{itemize}
\item Hinfuehren zum System
\item aus welchen arbeiten fuegt sich das system zusammen?
\item grober ueberblick ueber die einzelnen komponenten und sensoren
\item modulare uebersicht ueber das gesamte system. (denis bild + smoothing und prior)
\item particle filter mit formeluebersicht und was fusioniert wird
\section{Prior Arrangements}
System setup is very easily and no fingerprinting is required.
\begin{figure}[h!]
\centering%
\includegraphics[trim=99 0 0 0, clip, width=8.2cm]{editor1.png}%
\end{figure}
\end{itemize}
\cite{ebner-15}
\section{Prior Arrangements}
System setup is very easily and no fingerprinting is required.
\begin{itemize}
\item Map building: Grobe Beschreibung, Funktionen und Moeglichkeiten des Map Builders. bildchen

12
competition/tex/egbib.bib
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@@ -1725,7 +1725,7 @@ doi={10.1109/PLANS.2008.4570051},}
pages={1-10},
}
@inproceedings{Ebner-16,
@inproceedings{ebner-16,
author={Ebner, Frank and Fetzer, Toni and Grzegorzek, Marcin and Deinzer, Frank},
booktitle={19th Int. Conf. on Information Fusion (FUSION)},
title={{On Prior Navigation Knowledge in Multi Sensor
@@ -2734,4 +2734,14 @@ volume = {3},
year = {2009}
}
@inproceedings{fetzer-16,
author={Fetzer, Toni and Ebner, Frank and K{\"o}ping, Lukas and Grzegorzek, Marcin and Deinzer, Frank},
booktitle={Indoor Positioning and Indoor Navigation (IPIN), Int. Conf. on},
title={{On Monte Carlo Smoothing in Multi Sensor Indoor Localisation}},
year={2016},
IGNOREmonth={October},
pages={1-8},
}