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Vision-based dense displacement and strain estimation of miter gates with the performance evaluation using physics-based graphics models

  • Narazaki, Yasutaka (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign) ;
  • Hoskere, Vedhus (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign) ;
  • Eick, Brian A. (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign) ;
  • Smith, Matthew D. (Army Engineer Research and Development Center) ;
  • Spencer, Billie F. (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign)
  • Received : 2019.07.02
  • Accepted : 2019.08.29
  • Published : 2019.12.25
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Abstract

This paper investigates the framework of vision-based dense displacement and strain measurement of miter gates with the approach for the quantitative evaluation of the expected performance. The proposed framework consists of the following steps: (i) Estimation of 3D displacement and strain from images before and after deformation (water-fill event), (ii) evaluation of the expected performance of the measurement, and (iii) selection of measurement setting with the highest expected accuracy. The framework first estimates the full-field optical flow between the images before and after water-fill event, and project the flow to the finite element (FE) model to estimate the 3D displacement and strain. Then, the expected displacement/strain estimation accuracy is evaluated at each node/element of the FE model. Finally, methods and measurement settings with the highest expected accuracy are selected to achieve the best results from the field measurement. A physics-based graphics model (PBGM) of miter gates of the Greenup Lock and Dam with the updated texturing step is used to simulate the vision-based measurements in a photo-realistic environment and evaluate the expected performance of different measurement plans (camera properties, camera placement, post-processing algorithms). The framework investigated in this paper can be used to analyze and optimize the performance of the measurement with different camera placement and post-processing steps prior to the field test.

Keywords

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