deletions | additions       diff --git a/chapter_Introduction_label_chap_introduction__.tex b/chapter_Introduction_label_chap_introduction__.tex  index ebd33fb..2247914 100644  --- a/chapter_Introduction_label_chap_introduction__.tex  +++ b/chapter_Introduction_label_chap_introduction__.tex 
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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.If sufficient computational and physical power is available, a typical  approach to estimate a UAV's position is by using active laser  rangefinders~\cite{grzonka2009towards,bachrach2009autonomous}.  Although this approach is used in some simultaneous localization and  mapping (SLAM) frameworks, it is usually not feasible for MAVs because  they can carry only small payloads. A viable alternative are passive  computer vision techniques. Relying on visual information scales down  the physical payload since cameras are often significantly lighter  than laser  rangefinders~\cite{blosch2010vision,angeli20062d,ahrens2009vision}.  Additionally, many commercially available drones are already equipped  with cameras. In contrast to other existing approaches, the proposed algorithm does not rely on data from the inertial measurement unit (IMU). The only required tool is a camera, which makes the proposed algorithm rather safe to failure. Cameras are lightweight and not affected by external, such as magnetic fields. Additionally, relying on reduces the number of possible points of failure.