Improving the three-dimensional, structural velocity field reconstruction process with computer vision.

摘要

This research presents improvements to the velocity field reconstruction process achieved through computer vision. The first improvement of the velocity reconstruction process is the automation of the scanning laser Doppler vibrometer (SLDV) pose procedure. This automated process results in superior estimates of the position and orientation of the SLDV. The second improvement is the refinement of the formulation for reconstruction of the velocity field. The refined formulation permits faster computation, evaluation, and interpretation of the reconstructed structural velocity field. Taken together, these new procedures significantly improve the overall velocity reconstruction process which results in better, unbiased out-of-plane velocity estimates in the presence of noise.;The automation of the SLDV pose procedure is achieved through a computer vision model of the SLDV. The SLDV is modeled as a projective camera, i.e. an imager which preserves projectivities. This projective camera model permits the precise association of object features with image features. Specifically, circular features in the object space are seen by the SLDV as ellipses in the image space. In order to extract object points, the bitangents among the circular features are constructed and the bitangent points selected. The accuracy and precision of the object points are improved through the use of a calibrated object whose circular features are measured with a coordinate measuring machine. The corresponding image points are determined by constructing the bitangents among the ellipses and selecting the tangent points. Taken together, these object/image bitangent point sets are a significantly improved data set for previously developed SLDV pose algorithms. Experimental verification of this automated pose procedure includes demonstrated repeatability, independent validation of the estimated pose parameters, and comparison of the estimated poses with previous methods,;The refinement of the velocity reconstruction formulation is a direct result of the computer vision viewpoint adapted for this research. By viewing the velocity data as images of the harmonically excited structure's velocity field, analytical techniques developed for holographic interferometry are extended and applied to SLDV velocity images. Specifically, the “absolute” and “relative” fringe-order methods are used to reconstruct the velocity field with the “best” set of bases. Full and partial least squares solutions with experimental velocity data are calculated. Statistical confidence bounds of the regressed velocity coefficients are analyzed and interpreted to reveal accurate out-of-plane, but poor in-plane velocity estimates. Additionally, the reconstruction process is extended to recover the velocity field of a family of surfaces in the neighborhood of the “real” surface. This refinement relaxes the need for the exact experimental geometry. Finally, the velocity reconstruction procedure is reformulated so that independent least squares solutions are obtained for the two in-plane directions and the out-of plate direction. This formulation divides the original least squares problem into three smaller problems which can be analyzed and interpreted separately. These refinements to the velocity reconstruction process significantly improve the out-of-plane velocity solution and interpretation of the regressed velocity parameters.