diff --git a/competition/tex/chapters/barometer.tex b/competition/tex/chapters/barometer.tex index b54938e..05c92e2 100644 --- a/competition/tex/chapters/barometer.tex +++ b/competition/tex/chapters/barometer.tex @@ -1,6 +1,27 @@ \subsection{Barometer} - used if available - relative positioning (z) - relative to the first few measurements - also used to determine uncertainty + If available, the Smartphone's barometer is used to infer the likelyness of the current $z$-location% + % + As ambient pressure readings are highly influenced by environmental conditions + 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} + diff --git a/competition/tex/chapters/components.tex b/competition/tex/chapters/components.tex new file mode 100644 index 0000000..a24a0af --- /dev/null +++ b/competition/tex/chapters/components.tex @@ -0,0 +1,33 @@ +\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} + diff --git a/competition/tex/chapters/graph.tex b/competition/tex/chapters/graph.tex index 995e970..6dae1b7 100644 --- a/competition/tex/chapters/graph.tex +++ b/competition/tex/chapters/graph.tex @@ -1,7 +1,31 @@ \subsection{Transition} - a graph based on the building's floorplan - uses prior knowledge (floorplan + desired destination [if known]) - uses random-walks to perform the transition - uses imo (acc,gyro) for the random-walk (distance/direction) - calculated offline + To enhance the quality of the proposal distribution, the transition step is + based on a \SI{20}{\centimeter}-gridded graph $G = (V,E)$ + with vertices $v_i \in V$ and undirected edges $e_{i,j} \in E$ + derived from the buildings floorplan. This ensures that only valid + 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?} + + diff --git a/competition/tex/chapters/introduction.tex b/competition/tex/chapters/introduction.tex index b8c6942..124f278 100644 --- a/competition/tex/chapters/introduction.tex +++ b/competition/tex/chapters/introduction.tex @@ -5,7 +5,11 @@ \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 + \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} \cite{ebner-15} @@ -19,13 +23,9 @@ System setup is very easily and no fingerprinting is required. \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/wifi.tex} - \input{chapters/stepturn.tex} - \input{chapters/graph.tex} +\input{chapters/components.tex} + \begin{itemize} \item Fixed-lag smoother diff --git a/competition/tex/chapters/stepturn.tex b/competition/tex/chapters/stepturn.tex index 74e4711..42220dc 100644 --- a/competition/tex/chapters/stepturn.tex +++ b/competition/tex/chapters/stepturn.tex @@ -1,7 +1,11 @@ \subsection{Step- and Turn-Detection} - used for the transition step - uses the smartphone's imu (acc, gyro) - simple step detection using magnitudes - simple turn detection by integrating over the gyro - feeds the transition + The smartphone's IMU is used to track the number of steps the pedestrian has made + (accelerometer) as well as the relative heading change (gyroscope) since the last transition + \cite{ebner-15}.% + % + 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. + + diff --git a/competition/tex/chapters/wifi.tex b/competition/tex/chapters/wifi.tex index 7523037..a736e02 100644 --- a/competition/tex/chapters/wifi.tex +++ b/competition/tex/chapters/wifi.tex @@ -1,14 +1,18 @@ \subsection{\docWIFI} - absolute positioning using wifi (x,y,z) - no fingerprinting - uses signal-strength prediction model - position of access points must be known beforehand - only 2 parameters for all APs - only vague position estimation + the smartphone's \docWIFI{} component provides an absolute location estimation $(x,y,z)^T$ by + measuring the signal-strengths of nearby transmitters (\docAP{}) and comparing them with the signal-strengths + that should be measurable. The latter are determined using a signal-strength prediction model. + Thus, no fingerprinting is required. Solely the positions of the \docAP{}s must be known beforehand.% + % + 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} - optional - same as wifi - but only one param: as txp is broadcasted + If the building is equipped with \docIBeacon{}s, those may additionally be used as absolute location source. + Like \docWIFI{}, measurements are compared with a model prediction based on the known transmitter position. + The transmission power of the \docIBeacon{} is transmitted by the beacon itself and does not need any estimation beforehand. diff --git a/competition/tex/gfx/editor1.png b/competition/tex/gfx/editor1.png new file mode 100755 index 0000000..cc56ca8 Binary files /dev/null and b/competition/tex/gfx/editor1.png differ diff --git a/competition/tex/make.sh b/competition/tex/make.sh index 8a43477..523c4bf 100644 --- a/competition/tex/make.sh +++ b/competition/tex/make.sh @@ -1,12 +1,11 @@ PATH=$PATH:/mnt/data/texlive/bin/x86_64-linux/ -latex bare_conf.tex +pdflatex bare_conf.tex bibtex bare_conf -latex bare_conf.tex -latex bare_conf.tex +pdflatex bare_conf.tex +pdflatex bare_conf.tex -dvips bare_conf.dvi - -ps2pdf14 bare_conf.ps +#dvips bare_conf.dvi +#ps2pdf14 bare_conf.ps atril bare_conf.pdf&