Abstract (EN):
Digital Image Correlation (DIC) is a modern powerful tool to acquire displacement fields in both laboratory and field work. Still, a number of severe limitations persist that can hinder the application of the method to objects in field work, where perfect stability of the cameras or the object itself cannot be enforced, or image acquisition is to be performed during long periods and the equipment has to be moved in the meantime. A common example of this problem is found in large structure monitoring, such as auto or railway bridges, landslides, peers or any other structure that requires displacement monitoring due to damage, often associated with fatigue and fracture in metallic structures. Accurate detection of camera movement across images of the same surface enable the computation of 3D point clouds with correct camera calibration values and geometrically aligned with other point clouds from previous time instants. An algorithm, which uses feature detection to recalibrate a stereo camera-pair after movement, was developed in this work. The cameras are originally calibrated in their initial position, and the world coordinates of detected features in the background are computed to serve as reference. In subsequent image capture following camera setup movement, the new camera positions are computed using the new coordinates of the featured reference points, as well as newly detected point correspondences between images from both cameras. (C) 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ECF22 organizers.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
6