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.

Learning shape priors for single view reconstruction

In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.