changes by toni

tex/chapters/introduction.tex

@@ -20,7 +20,7 @@ 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 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 
+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 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}.
@@ -28,12 +28,11 @@ In our previous work we were able to present such a localisation system based on
 %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.
+Incorporating environmental knowledge is a necessary and gainful step e.g. 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. 
-Despite very good results, the system presented in \cite{Ebner-15} suffers from two drawbacks, we want to solve within this work.
+Despite very good results, the system presented in \cite{Ebner-15} suffers from two drawbacks, we attempt to solve within this work.
 
 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.
@@ -49,10 +48,9 @@ is not smoothly spread. The heading-change of our new approach is solely control
 During the random walk, matching edges are sampled according to their deviation from this change.
 
 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. 
+Such applications are utilized to navigate a user to his desired destination, limiting 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 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. 
+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.
 directly adhering to walls. 
@@ -61,7 +59,7 @@ Therefore, we present a novel approach that detects walls using an inverted grap
 
 Since areas near walls are less likely to be chosen for walking, a probabilistic weight is assigned to every node of the graph.
 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.
+Entering or leaving rooms is very unlikely as only a few nodes are representing doors and allow of doing so.
 This can be tackled by making such areas more likely.
 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}