added heading and step detection to transition

tex/chapters/abstract.tex

@@ -1,8 +1,8 @@
 \abstract{
 Within this work we present an updated version of our award-winning indoor localization system for smartphones. 
 The current position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. 
-Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access-points and signal strength predictions.
-Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access-points, we use an optimization scheme based on a few reference measurements to estimate a corresponding \docWIFI{} model.
+Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access points and signal strength predictions.
+Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a few reference measurements to estimate a corresponding \docWIFI{} model.
 To model the pedestrian's movement, which is constrained by walls and other obstacles, we propose a state transition based upon navigation meshes, modeling only the building's walkable areas.
 Continuous and smooth floor changes are enabled by using a simple activity recognition. 
 Our rapid computation scheme of the kernel density estimation allows to find an exact estimation of the pedestrian's current position. 

tex/chapters/experiments.tex

@@ -259,7 +259,7 @@ Walking down the stairs at \SI{80}{\second} does also recover the localization s
 	\label{fig:errorOverTimeWalk0}
 \end{figure}
 
-A similar behaviour as the above can be seen in walk 3. 
+A similar behavior as the above can be seen in walk 3. 
 Without a method to recover from impoverishment, the system lost track in \SI{100}{\percent} of the runs due to a not detected floor change in the last third of the walk. 
 By using the simple method, the overall error can be reduced and the impoverishment resolved. Nevertheless, unpredictable jumps of the estimation are causing the system to be highly uncertain in some situations, even if those jumps do not last to long.
 Only the use of the $D_\text{KL}$ method is able to produce reasonable results.
@@ -379,7 +379,7 @@ Nevertheless, even if both estimated paths look very different, they produce sim
 The purple square displays a situation in which a sample impoverishment was successfully resolved. 
 Due to a poorly working \docAPshort{}, in the lower corner of the big room the pedestrians passes before walking down the stairs, the majority of particles is dragged into the upper right corner of  that room and unable to walk down. 
 By allowing some particles to walk through the wall and thus down the stairs, the impoverishment could be dissolved. 
-The KDE-based estimation illustrates this behaviour very accurate.
+The KDE-based estimation illustrates this behavior very accurate.
 Another situation in which the estimated paths do not provide sufficient results can be seen inside the teal square. 
 The room is very isolated from the rest of the building, which is reflected by the fact that only 3 \docAPshort{}'s are detected.
 The pedestrians have been asked to cross the room at a quick pace, leading to a higher step rate and therefore update rate of the filter.

tex/chapters/introduction.tex

@@ -21,7 +21,7 @@ Within this work we present a simple yet efficient method that enables a particl
 We also use a novel approach for finding an exact estimation of the pedestrian's current position by using a rapid computation scheme of the kernel density estimation \cite{Bullmann-18}.
 
 Many historical buildings, especially bigger ones like castles, monasteries or churches, are built of massive stone walls and have annexes from different historical periods out of different construction materials.
-This leads to problems for methods using received signal strengths (RSS) from \docWIFI{} or Bluetooth, due to a high signal attenuation between different rooms. 
+This leads to problems for methods using received signal strengths indications (RSSI) from \docWIFI{} or Bluetooth, due to a high signal attenuation between different rooms. 
 Many unknown quantities, like the walls definitive material or thickness, make it expensive to determine important parameters, \eg{} the signal's depletion  over distance. 
 Additionally, most wireless approaches are based on a line-of-sight assumption. 
 Thus, the performance will be even more limited due to the irregularly shaped spatial structure of such buildings. 
@@ -35,10 +35,10 @@ Clearly, this is contrary to most costumers expectations of a fast to deploy and
 In addition, this is not only a question of costs incurred, but also for buildings under monumental protection, not allowing for larger construction measures. 
 
 To sum up, this work presents a smartphone-based localization system using a particle filter to incorporate different probabilistic models.
-We omit time-consuming approaches like classic fingerprinting or measuring the exact positions of access-points.
+We omit time-consuming approaches like classic fingerprinting or measuring the exact positions of access points.
 Instead we use a simple optimization scheme based on reference measurements to estimate a corresponding \docWIFI{} model.
 The pedestrian's movement is modeled realistically using a navigation mesh, based on the building's floorplan.
-A barometer based activity recognition enables going into the third dimension and problems occurring from multimodalities and impoverishment are taken into account. 
+A barometer and accelerometer based activity recognition enables going into the third dimension and problems occurring from multimodalities and impoverishment are taken into account. 
 
 The goal of this work is to propose a fast to deploy and low-cost localization solution, that provides reasonable results in a high variety of situations.
 Consequently, we believe that by utilizing our localization approach to such a challenging scenario, it is possible to prove those characteristics. 

tex/chapters/relatedwork.tex

@@ -4,7 +4,7 @@
 We consider indoor localization to be a time-sequential, non-linear and non-Gaussian state estimation problem. 
 Such problems are often solved using Bayesian filters, which update a state estimation recursively
 with every new incoming measurement. 
-A powerful method to obtain numerical results for this approach are particle filters.
+A powerful group of methods to obtain numerical results for this approach are particle filter.
 
 In context of indoor localization, particle filter approximate a probability distribution describing the pedestrian's possible whereabouts by using a set of weighted random samples (particles).
 Here, new particles are drawn according to some importance distribution, often represented by the state transition, which models the dynamics of the system.
@@ -21,25 +21,20 @@ Most localization approaches differ mainly in how the transition and evaluation
 
 The system's dynamics describe a pedestrian's potential movement within the building.
 This can be formulated as the question \emph{``Given the pedestrian's current position and heading are known, where could he be after a certain amount of time?''}.
-Obviously, the answer to this question depends on the pedestrian's walking behavior, any nearby architecture
-and thus the building's floorplan.
-
-Assuming the pedestrian to walk almost straight towards his current heading with a known, constant walking speed,
-the most basic form of state transition simply rejects all movements, where the line-of-sight
-between current position and potential destination is blocked by an obstacle \cite{Ebner-15}.
-
+Obviously, the answer to this question depends on the pedestrian's walking behavior, any nearby architecture and thus the building's floorplan.
+%
+Assuming the pedestrian to walk almost straight towards his current heading with a known, constant walking speed, the most basic form of state transition simply rejects all movements, where the line-of-sight between current position and potential destination is blocked by an obstacle \cite{Ebner-15}.
+%
 Despite its simplicity, this approach suffers from several drawbacks. 
 The intersection-test can be costly, depending on the number of used particles and the complexity of the building. 
 Furthermore, it is limited mainly to 2D transitions within the plane. 
 Smooth 3D transitions, like walking stairs, would require much more complex intersection tests \cite{Afyouni2012}.
 
-To overcome both limitations, the building's floorplan can be used to derive a graph-based structure,
-like voronoi diagrams or fixed-distance grids, moving all costly intersection tests into a one-time offline phase \cite{Ebner-16, Hilsenbeck2014}.
+To overcome both limitations, the building's floorplan can be used to derive a graph-based structure, like voronoi diagrams or fixed-distance grids, moving all costly intersection tests into a one-time offline phase \cite{Ebner-16, Hilsenbeck2014}.
 Hereafter, graph-based random walks along the created data-structure can be used as a fast transition approximation.
 Smooth transitions in 3D space can be achieved by generating nodes and edges along stairs and elevators.
 Furthermore, the nodes can be used to store additional information, like their distance towards a pedestrian's desired destination.
-Such information can be included during the transitions step, \eg{} increasing the likelihood of all potential movements that approach
-this destination \cite{Ebner-16}.
+Such information can be included during the transitions step, \eg{} increasing the likelihood of all potential movements that approach this destination \cite{Ebner-16}.
 
 However, the graph-based approach also imposes some potential issues. When using a gridded graph, the spacing between adjacent
 nodes directly represents the transition's accuracy. Likewise, the amount of required memory to represent the floorplan 
@@ -88,7 +83,7 @@ Additionally, we will show that such an optimization scheme can partly compensat
 
 %immpf
 Besides well chosen probabilistic models, the system's performance is also highly affected by handling problems which are based on the nature of particle filter.
-They are often caused by restrictive assumptions about the dynamic system, like the aforementioned sample impoverishment. 
+They are often caused by restrictive assumptions about the dynamic system, like seen from the aforementioned problem of sample impoverishment. 
 The authors of \cite{Sun2013} handled the problem by using an adaptive number of particles instead of a fixed one. 
 The key idea is to choose a small number of samples if the distribution is focused on a small part of the state space and a large number of particles if the distribution is much more spread out and requires a higher diversity of samples.
 The problem of sample impoverishment is then addressed by adapting the number of particles dependent upon the system's current uncertainty \cite{Fetzer-17}.

tex/chapters/transition.tex

@@ -22,7 +22,7 @@
 			\label{fig:museumMapMesh}
 		\end{subfigure}
 		\caption{
-			Floorplan and transition data structures for the lower floors of the building.
+			Floorplan and transition data structures for the ground floor of the building.
 			To reach every nook and cranny, the graph based approach (b) requires many nodes and edges.
 			The depicted version uses a coarse node-spacing of \SI{90}{\centi\meter} (1700 nodes) and barely reaches all doors and stairs.
 			A navigation mesh (c) requires only 320 triangles to tightly reach every corner within the building.
@@ -41,7 +41,7 @@
 	neighbors in the plane, creating a walkable graph for each floor. The resulting graphs are
 	hereafter connected via stairs or elevators, to form the final data structure
 	for the whole building.
-	This allowed for (semi-)random walks along the graph, by assigning probabilities to each edge,
+	This allowes for (semi-)random walks along the graph, by assigning probabilities to each edge,
 	using prior knowledge provided by sensors, forming the transition probability
 	$p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ \cite{Ebner-16}.
 	
@@ -110,7 +110,7 @@
 			\enskip.
 	\end{equation*}
 	
-	Whether the determined destination $(x_t, y_t)^T$ is actually reachable from the start $(x_{t-1}, y_{t-1})^T$ can be determined
+	Whether the newly obtained destination $(x_t, y_t)^T$ is actually reachable from the start $(x_{t-1}, y_{t-1})^T$ can be determined
 	by checking if their corresponding triangles are connected with each other.
 	If so, the corresponding $z_t$ can be interpolated using the barycentric coordinates of $(x_t, y_t)^T$
 	within a 2D projection of the triangle the position belongs to and applying them to the original 3D triangle.
@@ -121,6 +121,19 @@
 	that might be reachable. Increasing $\sigma_\text{step}$ and $\sigma_\text{turn}$ for those cases might also be a viable choice.
 	Likewise, just using some random position, omitting heading/steps might be viable as well.
 
+The detected steps $\mObsSteps$ and the heading change $\mObsHeading$ are obtained using the smartphones IMU. 
+To provide a robust heading change, we first need to rotate the gyroscope onto the east-north-up frame using a suitable transformation matrix.
+After the rotation, integrating over the gyros $z$-axis for a predefined time interval provides the user’s heading change (yaw) \cite{Ebner-15}.
+To obtain the matrix in the first place, we assume that the acceleration during walking is cyclic and thus the average acceleration over several cycles has to be almost zero. 
+This enables to measure the direction of gravity and use it to construct the transformation matrix. 
+It should be noted, that especially for cheap IMUs, as they can be found in most smartphones, the matrix has to be updated at very short intervals of one or two seconds to preserve good results \cite{davidson2017survey}.
+
+To receive the number of steps, we use a very simple step detection based on the accelerometer magnitude.
+For this, we calculated the difference between the average magnitude over the last \SI{200}{\milli\second} and the gravity vector.
+If this difference is above a certain threshold ($> \SI{0.32}{\m\per\square\s}$), a step is detected. 
+To prevent multiple detections within an unrealistic short interval, we block the complete process for \SI{250}{\milli\second} \cite{Koeping14}.
+
+	
 	%\commentByFrank{es gaebe noch ganz andere ansaetze etc. aber wir haben wohl nicht mehr genug platz :P}
 	%\commentByToni{ich denke aber auch, es langt.}
 

tex/egbib.bib

@@ -2970,3 +2970,14 @@ address = {{Rothenburg, Germany}},
 	title = {{Towards Cosmopolitan Robots : Intelligent Navigation in Extended Man-made Environments}}
 }
 
+@article{davidson2017survey,
+  title={{A Survey of Selected Indoor Positioning Methods for Smartphones}},
+  author={Davidson, Pavel and Pich{\'e}, Robert},
+  journal={IEEE Communications Surveys \& Tutorials},
+  volume={19},
+  number={2},
+  pages={1347--1370},
+  year={2017},
+  publisher={IEEE}
+}
+

tex/misc/keywords.tex

@@ -24,8 +24,8 @@
 
 \newcommand{\docsAP}{AP}
 \newcommand{\docAPshort}{AP}
-\newcommand{\docAP}{access-point}
-\newcommand{\docAPs}{access-points}
+\newcommand{\docAP}{access point}
+\newcommand{\docAPs}{access points}
 
 \newcommand{\docWIFI}{Wi\hbox{-}Fi}
 \newcommand{\docBeacon}{\Gls{Beacon}}