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\subsection{Optical Flow}  \label{sec:opticalflow}  Optical flow algorithms are biologically inspired methods for navigation---taking inspiration from insects and birds~\cite{ruffier2003bio}. They estimate the motion based on the shift of corresponding image keypoints in successive images.  Gradient based approaches, such as the Lucas-Kanade method, keypoint-based methods, and more specific methods have been put forth.Optical flow estimates the motion based on successive images: the shift of corresponding image keypoints in $x,y$-direction.  The approach is approaches are  computationally rather complex. It is a relative They belong to the class of local  localization method: without an initial reference point, it techniques and  can only estimate the distance position relative  to an initial reference point but not its absolute position in space. point.  The approach suffers approaches suffer  fromdrift: since the distance travelled is based on comparing successive images, errors are  accumulating errors  over time. The approach alone does time and typically do  not have the the possibility to correct provide a means for correcting  these errors. \citet{chao2013survey} compare different optical flow algorithms for the use with UAV navigation. To render on-board optical flow estimation feasible for small MAVs,  \citet{mcguire2016local} introduce a lightweight optical flow algorithm that is able to run on-board of an MAV. algorithm.  The algorithm uses compressed representations of images in the form of edge histogram to calculate the flow.