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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. The approaches are computationally rather complex. They belong to the class of local localization techniques and can only estimate the position relative to an initial reference point. The approaches suffer from accumulating errors over time and typically do not 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 odometry  feasible for small MAVs, \citet{mcguire2016local} introduce a lightweight algorithm. optical flow variant.  The algorithm uses compressed representations of images in the form of edge histogram to calculate the flow.