shortend

tex/chapters/conclusion.tex

@@ -13,8 +13,7 @@ prediction models e.g. incorporating wall information.
 As seen, multimodal distributions lead to faulty position estimations and therefore rising errors. 
 One possible method to resolve this issue would be a more suiting location estimation technique. 
 Another promising way is smoothing. 
-By deploying a fixed-lag smoother the system would still be perceived as real-time application,
-but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.
+By deploying a fixed-lag smoother the system would still be perceived as real-time application, but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.
 
 
 

tex/chapters/experiments.tex

@@ -21,8 +21,7 @@
 	its scans take much longer than those of the Motorola Nexus 6:
 	\SI{3500}{\milli\second} vs. \SI{600}{\milli\second}.
 	Also, the Nexus' barometer sensor provides readings both more frequent and far more accurate than
-	the Galaxy does. This results in a better localisation using the Nexus smartphone.
-	
+	the Galaxy does. This results in a better localisation using the Nexus smartphone.	
 	Despite being fast enough to run in realtime on the smartphone itself, computation was done offline using
 	the \mbox{CONDENSATION} particle filter with \SI{7500}{} particles as realization.
 	The weighted arithmetic mean of the particles was used as state estimation.

tex/chapters/grid.tex

@@ -107,7 +107,7 @@
 		%\commentByFrank{so besser? der ganze absatz.}
 		To downvote vertices near walls, we need to determine the distance of each vertex from its nearest wall.
 		We therefore derive an inverted version $G' = (V', E')$ of the graph $G$, just describing walls and 
-		obstacles. A nearest-neighbour search \cite{Cover1967} $\fNN{\mVertexA}{V'}$ within $V'$ provides the vertex
+		obstacles. A nearest-neighbour search $\fNN{\mVertexA}{V'}$ within $V'$ provides the vertex
 		nearest to $\mVertexA$.
 		%\begin{equation}
 		%	v' = \fNN{v}{V'} \enskip .

tex/chapters/introduction.tex

@@ -1,13 +1,16 @@
 \section{Introduction}
 
 Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our everyday life.
-Whether driving a car, jogging or shopping in the streets, GNSS-based applications simplify orientation,
-guide the way and even track our fitness achievements. But as soon as we drive into an underground car park or visit a shopping mall, most of them do not work at all.
-That is because satellite signals are too weak to pass through obstacles like ceilings.
-Moreover, their accuracy is not sufficient for individual parking spaces or office rooms.
+%Whether driving a car, jogging or shopping in the streets, 
+GNSS-based applications simplify orientation,
+guide the way and even track our fitness achievements. 
+%But as soon as we drive into an underground car park or visit a shopping mall, most of them do not work at all.
+%That is because satellite signals are too weak to pass through obstacles like ceilings.
+However, satellite signals are too weak to pass through obstacles like ceilings and their accuracy is not sufficient for most indoor tasks.
+%Moreover, their accuracy is not sufficient for individual parking spaces or office rooms.
 Therefore, many different solutions for localising a moving object within buildings have been developed in 
 the most recent years \cite{Ebner-15, Yang2015, Khaleghi2013, Fang09, Nurminen2014}.
-Especially the hard problem of pedestrian localisation and navigation has lately attracted a lot of interest. 
+Especially the hard problem of pedestrian navigation has lately attracted a lot of interest. 
 
 Most modern indoor localisation systems primarily use smartphones to determine the position of a pedestrian.
 Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth
@@ -15,16 +18,18 @@ are used to collect the necessary data.
 Additionally, environmental knowledge is often incorporated e.g. by using floormaps. 
 This combination of highly different sensor types is also known as sensor fusion.
 
-Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability distribution describing the pedestrian's possible whereabouts.
+Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability density describing the pedestrian's possible whereabouts.
 This procedure can be separated into two probabilistic models:
 The transition model, which represents the dynamics of the pedestrian and predicts his next accessible locations, and the evaluation model, which estimates the probability for the position also corresponding to 
 recent sensor measurements.
  %Therefore, the most accurate position is represented by a peak of the probability distribution. 
 In our previous work we were able to present such a localisation system based on all the sensors mentioned above, including the phone's barometer \cite{Ebner-15}.
 
-In pedestrian navigation, the human movement is subject to the characteristics of walking speed and -direction.
-Additionally, environmental restrictions need to be considered as well, for example, walking through walls is impossible.
-Therefore, incorporating environmental knowledge is a necessary and gainful step.
+%In pedestrian navigation, the human movement is subject to the characteristics of walking speed and -direction.
+%Additionally, environmental restrictions need to be considered as well, for example, walking through walls is impossible.
+%Therefore, incorporating environmental knowledge is a necessary and gainful step.
+Incorporating environmental knowledge is a necessary and gainful step. 
+For example walking through walls is impossible.
 Like other systems, we are using a graph-based approach to sample only valid movements.
 The unique feature of our approach is the way how human movement is modelled. 
 This is done by using random walks on a graph, which are based on the heading of the pedestrian. 
@@ -32,7 +37,7 @@ Despite very good results, the system presented in \cite{Ebner-15} suffers from
 
 First, the transition model of our previous approach uses discrete floor-changes.
 Although the overall system provides viable results, it does not resemble real-world floor changes.
-Especially the barometric sensor is affected due to its continuous pressure measurements.
+Especially the barometer is affected due to its continuous pressure measurements.
 The discrete model prevents the barometer's full potential.
 It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions.
 To address this problem we extended the graph by adding realistic stairs, allowing a step-wise transition in the $z$-direction.
@@ -46,7 +51,7 @@ During the random walk, matching edges are sampled according to their deviation
 To improve the complex problem of localising a person indoors, prior knowledge given by a navigation system can be used.
 Such applications are used to navigate a user to his desired destination.
 This limits the unpredictability of human movement to a certain degree. 
-So, based on this assumption, the destination is known beforehand and the starting point is the pedestrian's currently estimated position. 
+So, based on this assumption, the destination is known beforehand and the starting point is the user's currently estimated position. 
 Regarding a graph-based transition model, one could suggest to use the shortest route between start and destination as the user's most-likely-to-walk path. 
 By incorporating this prior knowledge into the state transition step, a new state can be sampled in a more targeted manner. 
 However, for regularly tessellated (grid) graphs, as used in \cite{Ebner-15}, this would lead to unnatural paths e.g.
@@ -58,7 +63,7 @@ Since areas near walls are less likely to be chosen for walking, a probabilistic
 This allows a variety of options for integrating additional knowledge about the environment and enables us to address another problem:
 Entering or leaving rooms is very unlikely as only a few nodes are representing doors and allow doing so.
 This can be tackled by making such areas more likely.
-Therefore, a novel approach for detecting doors using the inverted graph and a principal component analysis (PCA) \cite{Hotelling1933} is presented within this work.
+Therefore, a novel approach for detecting doors using the inverted graph is presented within this work.
 %\commentByFrank{auch hier vlt das inverted erstmal noch weg lassen wegen platz}
 
 Finally, it is now possible to calculate more natural and realistic paths using the weighted graph. 

tex/chapters/relatedwork.tex

@@ -11,7 +11,7 @@ use signal strength prediction models like the log-distance or wall-attenuation-
 Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem. Using
 Kalman filters is therefore problematic \cite{sarkka2013bayesian, Nurminen2014}.
 All this shows, that sensor models differ in many ways and are a subject in itself.
-A good discussion on different sensor models can be found in \cite{Yang2015}, \cite{Gu2009} or \cite{Khaleghi2013}. 
+A good discussion on different sensor models can be found in \cite{Yang2015} or \cite{Khaleghi2013}. 
 
 However, within this work, we use simple models, configured using a handful of empirically chosen parameters and
 address their inaccuracies by harnessing prior information like the pedestrian's desired destination. Therefore,
@@ -21,7 +21,7 @@ on the state transition and how to incorporate environmental and navigational kn
 A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position
 using both, a walking direction and a to-be-walked distance, starting from the previous position.
 If the line-of-sight between the new and the old position intersects a wall, the probability for this
-transition is set to zero \cite{Woodman08-PLF, Blanchert09-IFF, Koeping14-ILU}.
+transition is set to zero \cite{Blanchert09-IFF, Koeping14-ILU}.
 However, as \cite{Nurminen13-PSI} already stated, it "gives more probability to a short step".
 An additional drawback of these approaches is that for every transition an intersection-test
 must be executed and thus often yields a high computational complexity.
@@ -34,15 +34,12 @@ It represents the topological skeleton of the building's floorplan as an irregul
 This drastically removes degrees of freedom from the map, and results in a low complexity.
 
 In the work of \cite{Nurminen2014} a Voronoi diagram is used to approximate the human movement. 
-It is assumed that the pedestrian can be anywhere on the topological links.
+It is assumed that the user can be anywhere on the topological links.
 The probabilities of changing to the next link are proportional to the total link lengths.
-However, for highly accurate localisation in large-scale buildings, this network of one-dimensional
-curves is not suitable \cite{Afyouni2012}.
-Therefore, \cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram
-by adding those two-dimensional areas.
-The final graph is then created by sampling nodes in regular intervals across the links and filling up the open
-spaces in a tessellated manner.  Similar to \cite{Ebner-15}, they provide a state transition model that selects
-an edge and a node from the graph according to a sampled distance and heading. 
+However, for accurate localisation in large-scale buildings, this network of one-dimensional curves is not suitable \cite{Afyouni2012}.
+Therefore, \cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram by adding those two-dimensional areas.
+The final graph is then created by sampling nodes in regular intervals across the links and filling up the open spaces in a tessellated manner.  
+Similar to \cite{Ebner-15}, they provide a transition model that selects an edge and a node from the graph according to a sampled distance and heading. 
 
 Nevertheless, most corridors are still represented by just one topological link. 
 While the complexity is reduced, it does not allow arbitrary movements and leads to suboptimal trajectories. 
@@ -74,7 +71,7 @@ An additional smoothing procedure is performed to make the path more natural.
 They are considering foot span, body dimensions and obstacle dimensions when determining whether an obstacle is surmountable. 
 However, many of this information is difficult to ascertain in real-time or imply additional effort in real-world environments. 
 Therefore, more realistic simulation models, mainly for evacuation simulation, are just using a simple shortest path on regularly
-tessellated graphs \cite{Sun2011, tan2014agent}. A more costly, yet promising approach is shown by \cite{Brogan2003}. They use a
+tessellated graphs \cite{tan2014agent}. A more costly, yet promising approach is shown by \cite{Brogan2003}. They use a
 data set of previously recorded walks to create a model of realistic human walking paths. 
 
 Finally, it seems that currently none of the localisation system approaches are using realistic walking paths as additional