Smart Structures and Systems

  • 제13권6호
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  • Pages.927-942
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  • 2014
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Vision-based remote 6-DOF structural displacement monitoring system using a unique marker

  • Jeon, Haemin (Department of Civil and Environmental Engineering, KAIST) ;
  • Kim, Youngjae (Robotics Program, KAIST) ;
  • Lee, Donghwa (Department of Civil and Environmental Engineering, KAIST) ;
  • Myung, Hyun (Department of Civil and Environmental Engineering, KAIST)
  • 투고 : 2013.11.20
  • 심사 : 2014.04.15
  • 발행 : 2014.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Structural displacement is an important indicator for assessing structural safety. For structural displacement monitoring, vision-based displacement measurement systems have been widely developed; however, most systems estimate only 1 or 2-DOF translational displacement. To monitor the 6-DOF structural displacement with high accuracy, a vision-based displacement measurement system with a uniquely designed marker is proposed in this paper. The system is composed of a uniquely designed marker and a camera with a zooming capability, and relative translational and rotational displacement between the marker and the camera is estimated by finding a homography transformation. The novel marker is designed to make the system robust to measurement noise based on a sensitivity analysis of the conventional marker and it has been verified through Monte Carlo simulation results. The performance of the displacement estimation has been verified through two kinds of experimental tests; using a shaking table and a motorized stage. The results show that the system estimates the structural 6-DOF displacement, especially the translational displacement in Z-axis, with high accuracy in real time and is robust to measurement noise.

키워드

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피인용 문헌

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  3. Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges vol.17, pp.10, 2017, https://doi.org/10.3390/s17102317
  4. Multi-point displacement monitoring of bridges using a vision-based approach vol.20, pp.2, 2015, https://doi.org/10.12989/was.2015.20.2.315
  5. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications vol.2016, 2016, https://doi.org/10.1155/2016/7103039
  6. Vision-based structural displacement measurement: System performance evaluation and influence factor analysis vol.88, 2016, https://doi.org/10.1016/j.measurement.2016.01.024
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  10. Vision-based hybrid 6-DOF displacement estimation for precast concrete member assembly vol.20, pp.4, 2014, https://doi.org/10.12989/sss.2017.20.4.397
  11. Non-Contact Measurement of the Surface Displacement of a Slope Based on a Smart Binocular Vision System vol.18, pp.9, 2014, https://doi.org/10.3390/s18092890
  12. Non-Target Structural Displacement Measurement Using Reference Frame-Based Deepflow vol.19, pp.13, 2019, https://doi.org/10.3390/s19132992
  13. Port Structure Inspection Based on 6-DOF Displacement Estimation Combined with Homography Formulation and Genetic Algorithm vol.11, pp.14, 2021, https://doi.org/10.3390/app11146470