deletions | additions       diff --git a/chapter_Introduction_label_chap_introduction__.tex b/chapter_Introduction_label_chap_introduction__.tex  index b363685..eceb282 100644  --- a/chapter_Introduction_label_chap_introduction__.tex  +++ b/chapter_Introduction_label_chap_introduction__.tex 
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%(MAVs) for surveillance. To date, indoor employment of these vehicles  %is still hindered by several limitations. The focus of this thesis is,  %thus, the development of accurate and fast indoor localization for  %MAVs combining computer vision and machine learning techniques.Since precision and reliability are crucial for safe flight, autonomous indoor navigation of an MAV is a  challenging task. While unmanned aerial vehicles (UAVs) for  outdoor usage can rely on the global positioning system (GPS), this system is  usually not available in confined spaces and would not provide  sufficiently accurate estimates in cluttered environments.