deletions | additions       diff --git a/subsection_Homography_Determination_Keypoint_Matching__.tex b/subsection_Homography_Determination_Keypoint_Matching__.tex  index d1705ee..a1698cc 100644  --- a/subsection_Homography_Determination_Keypoint_Matching__.tex  +++ b/subsection_Homography_Determination_Keypoint_Matching__.tex 
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A standard approach for estimating camera pose is detecting and  describing keypoints of the current view and a reference image, using  algorithms such as \textsc{Sift}~\cite{lowe1999object}, followed by finding a homography between both keypoint sets. A  keypoint is a salient image location that location---described by a feature vector---that  is invariant to different viewing angles and scaling. Keypoints are described by a feature vector. scaling .  By finding a homography, that is a perspective transformation between the keypoints of the current view and a reference image, the current view can be located in the reference image. The %The  $3 \times 3$ homography %homography  matrix ($H$) is based on at least four keypoint matches between %between  both images. However, usually more points are available, leading %leading  to an overdetermined equation. An initial homography matrix is then %then  created using a least-squares approach and further refined by various %various  algorithms. While this approach is used in frameworks for visual simultaneous localization and mapping, the pipeline of feature detection, description, matching, and pose estimation is computationally complex. Therefore, ground stations for off-board processing or larger processors are usually needed for flight control.