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Vision-based egomotion estimation can be employed to endow with navigation ability an Unmanned Aerial Vehicle (UAV) that is equipped with an on-board camera. The egomotion estimation block computes the 3D UAV motion, taking as an input a 2D optical flow map that is constructed for each of the captured video frames. This work considers sparse optical flow estimation, and thus the navigation system that is developed includes a feature selection unit, which initially identifies the points of the optical flow map. This paper demonstrates that the feature selection process, and in particular the geometry of the selected feature set, decisively determines the overall system performance. Various computation schedules, which combine geometric constraints with a textural quality metric for the image features, are thus investigated. This paper shows that imposing appropriate distance constraints in the feature selection process significantly increases the output precision of the egomotion estimation unit, thus enabling accurate vision-based UAV self-navigation.