Design considerations for a micro aerial vehicle aerodynamic characterization facility at the University of Florida research and engineering education facility

This paper describes the design considerations for a proposed aerodynamic characterization facility (ACF) for micro aerial vehicles (MAVs). This is a collaborative effort between the Air Force Research Laboratory Munitions Directorate (AFRL/MN) and the University of Florida Research and Engineering Education Facility (UF/REEF). The ACF is expected to provide a capability for the characterization of the aerodynamic performance of future MAVs. This includes the ability to gather the data necessary to devise control strategies as well as the potential to investigate aerodynamic 'problem areas' or specific failings. Since it is likely that future MAVs will incorporate advanced control strategies, the facility must enable researchers to critically assess such novel methods. Furthermore, the aerodynamic issues should not be seen (and tested) in isolation, but rather the facility should be able to also provide information on structural responses (such as aeroelasticity) as well as integration issues (say, thrust integration or sensor integration). Therefore the mission for the proposed facility ranges form fairly basic investigations of individual technical issues encountered by MAVs (for example an evaluation of wing shapes or control effectiveness) all the way to testing a fully integrated vehicle in a flight configuration for performance evaluation throughout the mission envelope.

Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure

Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds.© 2012 Elsevier Ltd. All rights reserved.