added missing legend to gfx

fixed some typos and refactored some sentences

tex/chapters/relatedwork.tex

@@ -7,7 +7,7 @@ They differ mainly by the used sensors, their probabilistic models and how envir
 For example \cite{Li2015} recently presented an approach combining methods of pedestrian dead reckoning (PDR), \docWIFI{}
 fingerprinting and magnetic matching using a Kalman filter. While providing good results, fingerprinting methods
 require an extensive offline calibration phase. Therefore, many other systems like \cite{Fang09} or \cite{Ebner-15}
-are using signal strength prediction models like the log-distance model or wall-attenuation-factor model.
+use signal strength prediction models like the log-distance model or wall-attenuation-factor model.
 Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem, what makes the
 usage of Kalman filters problematic \cite{sarkka2013bayesian, Nurminen2014}.
 All this shows, that sensor models differ in many ways and are a subject in itself.
@@ -31,7 +31,7 @@ Here, two main classes can be distinguished: symbolic and geometric spatial mode
 Especially geometric spatial models (coordinate-based approaches) are very popular, since they integrate metric properties to provide highly accurate location and distance information.
 One of the most common environmental representations in indoor localization literature is the Voronoi diagram \cite{Liao2003}. 
 It represents the topological skeleton of the building's floorplan as an irregular tessellation of space. 
-This drastically removes degrees of freedom from the map, what results in a low complexity.
+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.
@@ -58,7 +58,7 @@ or the behaviour of a pedestrian at this particular position (e.g. jumping or ru
 
 A similar approach is presented in \cite{Li2010}, \cite{Ebner-15} and is also used within this work. 
 By assuming that the floorplan is given beforehand, the occupied cells can be removed. 
-The remaining cells are described by its centre and represent all free spaces in the indoor environment. 
+The remaining cells are described by their centre/bounding-box and represent all free spaces in the indoor environment. 
 A graph is defined by using the centres as nodes and connecting direct neighbours with edges. 
 
 In order to enable floor changes, some approaches suggest to simply connect the nodes at staircases \cite{Ebner-15, Hilsenbeck2014}. 
@@ -73,9 +73,9 @@ For example, \cite{Bandi2000} uses an A* algorithm to search a 3D gridded enviro
 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 regular
+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
-data set of previous recorded walks to create a model of realistic human walking paths. 
+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
 source of information to provide a more targeted and robust movement. Most common systems are sampling a new state only in