notes from toni

tex/chapters/abstract.tex

@@ -14,4 +14,6 @@ The evaluation of the system within a $\SI{77}{\meter}$ $\times$ $\SI{55}{\meter
 shows that high accuracy can be achieved while also keeping the update-rates low. 
 \commentByFrank{We will show that incorporating prior knowledge, such as the pedestrian's desired destination,
 improves the overall localisation process and prevents various error-conditions.}
+
+\commentByToni{Das ist der alte Abstract vom letzten Paper. :D Da wollte ich noch nen ganz neuen schreiben. Das mach ich aber immer gaaaanz am ende}
 \end{abstract}

tex/chapters/introduction.tex

@@ -1,13 +1,11 @@
 \section{Introduction}
 
-Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our lives.
-\commentByFrank{everyday life?}
+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 are making orientation easier,
-point the way and even track our fitness achievements. But as soon as we drive into an underground car park or visit a shopping mall,
-they perform poorly \commentByFrank{most of them do not work at all?}.
-That is because satellite signals are to weak to pass through obstacles like buildings' walls \commentByFrank{floors. kommen ja von oben}.
-Moreover, their accuracy is not sufficient for individual parking spaces or rooms \commentByFrank{rooms? im parkhaus?}.
-Therefore, many different solutions for localizing a moving object within buildings have been developed in \commentByFrank{in the most recent?} recent years \cite{}.
+point 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 to weak to pass through obstacles like buildings' ceilings.
+Moreover, their accuracy is not sufficient for individual parking spaces or office rooms.
+Therefore, many different solutions for localizing a moving object within buildings have been developed in most recent years \cite{Ebner-15, Yang2015, Khaleghi2013, Fang09, Nurminen2014}.
 Especially the hard problem of pedestrian localization and navigation has lately attracted a lot of interest. 
 
 Most modern indoor localisation systems primarily use smartphones for determining the position of a pedestrian.
@@ -16,26 +14,28 @@ are used for collecting the necessary data. Additionally, environmental knowledg
 floor maps. 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 uncertainties of the system.
-\commentByFrank{interessieren uns die unsicherheiten, oder eher die wahrscheinlichkeit des hidden sate?
-describing the pedestrian's possible whereabouts?}
+distribution describing describing the pedestrian's possible whereabouts.
 
 This procedure can be separated into two probabilistic models:
-The transition model represents the dynamics of the system \commentByFrank{eher pedestrian? den modellieren wir ja}
+The transition model represents the dynamics of the pedestrian
 and predicts the next accessible locations,
 while the evaluation model estimates the probability for the position also corresponding to the 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 above mentioned
 sensors including the phone's barometer \cite{Ebner-15}.
-\commentByFrank{das baro ist schon wieder einzeln aufgezaehlt?}
 
 In pedestrian navigation, the human movement underlies the characteristics of walking speed and walking direction.
 Additionally, environmental restrictions need to be considered as well, for example, walking through walls is in
 most cases impossible. Therefore, incorporating environmental knowledge is a necessary and gainful step.
 Like other systems, we are using a graph-based approach to sample only valid locations.
 The unique feature of our approach is the way in how we model the human movement.
-This is done by using random walks on graphs \commentByFrank{the graph?}, which are based upon the heading of the
-pedestrian. However, the system presented in \cite{Ebner-15} suffers from two major drawbacks, we want to solve within this work.
+This is done by using random walks on a graph, which are based upon the heading of the
+pedestrian. 
+Despite very good results
+
+However, the system presented in \cite{Ebner-15} suffers from two major drawbacks, we want to solve within this work.
+
+
 \commentByFrank{unser unique feature ist also, dass es nicht geht? :P so liest sich der absatz}
 
 Firstly, the transition model of our past \commentByFrank{previous?} approach uses discrete floors.

tex/chapters/sensors.tex

@@ -29,6 +29,7 @@
 			\label{eq:baroTransition}
 		\end{equation}
 		
+		\commentByToni{Woher kommen die 0.105hPa? Sollte man dazu schreiben.}
 		The evaluation following the transition then compares the predicted relative pressure with the observed one
 		using a normal distribution with the previously estimated $\sigma_\text{baro}$:
 		
@@ -77,7 +78,7 @@
 	\subsection{Step- \& Turn-Detection}
 		
 		To prevent degradation within the particle-filter \cite{??} due to downvoting of particles with increased
-		heading deviation, we incorporate the turn-detection as control-data directly into the transition
+		heading deviation, we incorporate the turn-detection as control-data \commentByToni{ich würde es jetzt nicht unbedingt  controldata nennen. einfach nur das wir die observation in die transition integrieren. fertig.} directly into the transition
 		$p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$.
 		\cite{thrun?}\cite{lukas2014?} to get a more directed sampling instead of a truly random one.