revision 02

tex_review/chapters/experiments.tex

@@ -101,13 +101,14 @@ Nevertheless, Android app and offline application are both use the same C++ back
 %Sensor measurements are recorded using a simple mobile application that implements the standard Android SensorManager. 
 
 The experiments are separated into five sections:
-At first, we discuss the performance of the novel transition model and compare it to a grid-based approach.  
+At first, we discuss the performance of the novel transition model and compare it to our previous approach using a gridded graph structure.  
 In section \ref{sec:exp:opti} we have a look at \docWIFI{} optimization and how the real \docAPshort{} positions differ from it. 
 Following, we conducted several test walks throughout the building to examine the estimation accuracy (in meter) of the localization system and discuss the here presented solutions for sample impoverishment.
 \add{In section \ref{sec:eval:act} the threshold-based activity recognition is evaluated, providing a detection rate for the test walks utilized before.}
 Finally, the respective estimation methods are discussed in section \ref{sec:eval:est}.
 
 \subsection{Transition}
+\label{sec:exp:transition}
 
 \begin{figure}[t]
 	\centering
@@ -171,9 +172,9 @@ For example walking through a door, would result in a strong reduction of differ
 If the state evaluation is then used to assign weights to particles, the crucial problem of sample degeneracy often occurs. 
 With a mesh, on the other hand, walkable destinations can also be located in a room behind a wall.
 In combination with the continues movement, this allows for a high versatility of particles even in such situations. 
-Another method to fix the problems shown in fig. \ref{fig:transitionEval:d}, is by adding an activity recognition (walking up, down straight) or to incorporate a barometer. 
-Nevertheless, in most cases it is an advantage if a sensor model delivers good results on its own, without further dependencies. 
-For example, if a sensor is currently unavailable or damaged, the system is still able to provide prober results. 
+Another method to fix the problems shown in fig. \ref{fig:transitionEval:d}, is by adding an activity recognition (walking up, down, straight) or to incorporate a barometer. 
+Nevertheless, in most cases it is an advantage, if a sensor model delivers good results on its own, without further dependencies. 
+For example, if a sensor is currently unavailable or damaged, the system is still able to provide proper results. 
 
 Besides the advantages the mesh offers, it also has a few disadvantages compared to the graph.
 The computation time has increased due to the calculation of reachable destinations. 
@@ -299,6 +300,13 @@ In contrast, the $D_\text{KL}$-based method extends the transition and thus uses
 We set $l_\text{max} =$ \SI{-75}{dBm} and $l_\text{min} =$ \SI{-90}{dBm}.
 For a better overview, we only used the KDE-based estimation, as the errors compared to the weighted-average estimation differ by only a few centimeter. 
 
+\addy{The same applies for an accuracy comparison between the graph-based model and the navigation mesh as part of the overall system.
+Both provide very similar localization errors regarding the conducted walks.
+This is not a big surprise, as the accuracy of the pedestrian’s position based on the estimated state and thus the complete posterior density (weighted particle set).
+It is obvious, that choosing a graph with a grid-size of e.g. \SI{2}{} x \SI{2}{\meter} would worsen the results.
+This leads to the statement, that the approximation error of walking alongside the edges of a (reasonable sized) gridded graph is small enough that it has no significant influence on the overall localization accuracy compared to a true continuous motion. 
+Nevertheless, as shown in section \ref{sec:exp:transition}, the navigation mesh offers several major benefits by highly reducing the memory footprint.}
+
 \begin{table}[t]
 	\centering
 	\begin{tabular}{rrrrcrrrcrrr}

tex_review/chapters/introduction.tex

@@ -49,11 +49,11 @@ In the here presented scenario, the beacons do not establish a wireless network
 
 %Im Gegensatz zu vielen anderen Arbeiten 
 
-To sum up, \add{this work presents an updated version of the winning localization system of the smartphone-based competition at IPIN 2016 \cite{Ebner-15}, including the improvements and newly developed methods that have been made since then \cite{Ebner-16, Ebner-17, Fetzer-17, Bullmann-18}.
+To sum up, \add{this work presents an updated version of the winning localization system of the smartphone-based competition at IPIN 2016 \cite{competitionSpain, Fetzer-16}, including the improvements and newly developed methods that have been made since then \cite{Ebner-16, Ebner-17, Fetzer-17, Bullmann-18}.
 This is the first time that all these previously acquired findings have been fully combined and applied simultaneously.
 During the here presented update, the following contributions will be presented and added to the system: 
 \begin{itemize}
-	\item The pedestrian's movement is modelled in a more realistic way using a navigation mesh, generated from the building's floor plan. This only allows movements that are actually feasible, e.g. no walking through walls. Compared to the gridded-graph structure we used before \cite{Ebner-16}, the mesh allows continuous transitions and reduces the required storage space drastically.
+	\item The pedestrian's movement is modelled in a more realistic way using a navigation mesh, generated from the building's floor plan. This only allows movements that are actually feasible, e.g. no walking through walls. Compared to the gridded graph structure we used before \cite{Ebner-16}, the mesh allows continuous transitions and reduces the required storage space drastically.
 	\item To enabled more smooth floor changes, a threshold-based activity recognition using barometer and accelerometer readings is added to the state evaluation process of the particle filter. The method is able to distinguish between standing, walking, walking up and walking down. 
 	\item To address the problem of sample impoverishment in a wider scope, we present a simplification of our previous method \cite{Fetzer-17}. This reduces the overhead of adapting an existing system to the proposed method and allows to incorporate it directly to the state transition of any approach, using a general particle filter methodology.
 \end{itemize} 

tex_review/chapters/transition.tex

@@ -108,10 +108,10 @@
 		\addy{However, while a graph restricts the movement to edges and nodes, the mesh allows for a 
 		true continues movement. 
 		This is achieved by having the freedom to walk to any position, under the condition that it 
-		resides within a polygon.}
+		resides within a polygon that is actually walkable from the starting position.}
 		\add{Just as before, the navigation mesh can be \emph{automatically}
 		generated from the building's floor plan, based on
-		various algorithms \cite{navMeshAlg1, kallmann2010navigation}.
+		various algorithms \cite{navMeshAlg1, kallmann2010navigation, van2011navigation}.
 		}
 
 	Using variably shaped/sized elements instead of rigid grid-cells
@@ -138,8 +138,9 @@
 		For the example of the rectangular room, two adjacent triangles are required to form
 		a rectangular shape.
 		However, using triangles, operations such as aforementioned contains-check, can now easily be performed,
-		\eg{} by using barycentric coordinates, yielding noticeable speedups compared to polygons.
-	}
+		\eg{} by using barycentric coordinates, yielding noticeable speedups compared to polygons.} 
+	\addy{This approach has established itself especially in the field of computer game development for solving pathfinding problems. 
+		A popular open-source library for creating navigation meshes in C++ is Recast \cite{Recast}.}
 	
 	\newcommand{\turnNoise}{\mathcal{T}}
 	\newcommand{\stepSize}{\mathcal{S}}

tex_review/egbib.bib

@@ -2940,9 +2940,17 @@ address = {{Rothenburg, Germany}},
 @misc{Wemos,
 title = {{WEMOS Electronics}},
 note = {\url{https://www.wemos.cc/}, Accessed: 2018-03-22},
-address = {{Rothenburg, Germany}},
+IGNOREaddress = {{Rothenburg, Germany}},
 }
 
+@misc{Recast,
+title = {{Recast Navigation}},
+author={Mononen, Mikko},
+note = {\url{https://github.com/recastnavigation/}, Accessed: 2018-11-08},
+IGNOREaddress = {{Helsinki, Finland}},
+}
+
+
 @article{torres2017smartphone,
   title={The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work},
   author={Torres-Sospedra, Joaqu{\'\i}n and Jim{\'e}nez, Antonio R and Knauth, Stefan and Moreira, Adriano and Beer, Yair and Fetzer, Toni and Ta, Viet-Cuong and Montoliu, Raul and Seco, Fernando and Mendoza-Silva, Germ{\'a}n M and others},
@@ -3662,5 +3670,26 @@ IGNOREmonth={Oct},}
   publisher={ACM}
 }
 
+@inproceedings{van2011navigation,
+  title={{Navigation Meshes for Realistic Multi-Layered Environments}},
+  author={Van Toll, Wouter and Cook, Atlas F and Geraerts, Roland},
+  booktitle={Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on},
+  pages={3526--3532},
+  year={2011},
+  organization={IEEE}
+}
+
+
+@Article{competitionSpain,
+AUTHOR = {Potortì, Francesco and Park, Sangjoon and Jiménez Ruiz, Antonio Ramón and Barsocchi, Paolo and Girolami, Michele and Crivello, Antonino and Lee, So Yeon and Lim, Jae Hyun and Torres-Sospedra, Joaquín and Seco, Fernando and Montoliu, Raul and Mendoza-Silva, Germán Martin and Pérez Rubio, Maria Del Carmen and Losada-Gutiérrez, Cristina and Espinosa, Felipe and Macias-Guarasa, Javier},
+TITLE = {{Comparing the Performance of Indoor Localization Systems through the EvAAL Framework}},
+JOURNAL = {Sensors},
+VOLUME = {17},
+YEAR = {2017},
+NUMBER = {10},
+ARTICLE-NUMBER = {2327},
+}
+
+