my_thesis.tex

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ngerman, % (ngerman/english) internationalisation
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]{iib_thesis}


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\title{Pose Estimation in Gebäuden anhand von Convolutional Neural Networks und simulierten 3D-Daten}
\author{Abdullah Sahin}
\degree{Bachelor}
\studyprogram{Angewandte Informatik}
\date{3. September 2019}
\matno{108016202304}
\firstadviser{Prof. Dr.-Ing. Markus König}
\secondadviser{Patrick Herbers, M.\,Sc.}

\myabstract{The ability of indoor pose estimation is an essential key component for a wide range of applications in the construction industry, such as progress monitoring of finished works or augmented reality-based maintenance and navigation of indoor facilities. There are numerous indoor localization methods based on different technologies, with an image-based approach via a mobile device being the most economical. This work aims to investigate an existing image-based approach using convolutional neural networks trained on synthetic images to estimate the pose of real images. Four datasets of indoor scenes from two buildings with various trajectory properties were created and independently analyzed. The average position accuracy was 11$m$, while an accuracy about 1$m$ could be achieved by training and testing the artificial neural networks with a dataset from the same domain. Moreover, the networks using cross-domain data localized all test images in a confined area and estimated the orientation mostly as the same one.}



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%\includeonly{sections/section_5, sections/section_6 }
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\include{sections/section_2}
\include{sections/section_3}
\include{sections/section_4}
\include{sections/section_5}
\include{sections/section_6}

\end{document}