Proceedings Article | 1 April 2016
Proc. SPIE. 9805, Health Monitoring of Structural and Biological Systems 2016
KEYWORDS: Detection and tracking algorithms, Cameras, LIDAR, Calibration, Remote sensing, Inspection, Clouds, 3D modeling, Computer vision technology, 3D metrology, Finite element methods, Machine vision, Reconstruction algorithms, Algorithm development, 3D vision
Modern remote sensing technologies have enabled the creation of high-resolution 3D point clouds of infrastructure systems. In particular, photogrammetric reconstructions using Dense-Structure-from-Motion algorithm can now yield point clouds with the necessary resolution to capture small-strain displacements. By tracking changes in these point clouds over time, displacements can be measured, leading to strain and stress estimates for long-term structural evaluations. This study determines the accuracy of a comparative point cloud analysis technique for measuring deflections in high-resolution point clouds of structural elements. Utilizing a combination of a recently developed point cloud generation process and localized nearest-neighbors cloud comparisons, the analytical technique is designed for long-term field scenarios and requires no artificial tracking, targets, and camera calibrations. A series of flexural laboratory experiments were performed in order to test the approach. The results indicate sub-millimeter accuracy in measuring the vertical deflection, making it suitable for the small-displacement analysis of a variety of large-scale infrastructure systems. Ongoing work seeks to extend this technique for comparison with as-built and finite element models.
