Smart Structures and Systems

  • 제14권5호
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  • Pages.943-960
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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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Pose-graph optimized displacement estimation for structural displacement monitoring

  • Lee, Donghwa (Department of Civil and Environmental Engineering, KAIST) ;
  • Jeon, Haemin (Department of Civil and Environmental Engineering, KAIST) ;
  • Myung, Hyun (Department of Civil and Environmental Engineering, KAIST)
  • 투고 : 2013.09.03
  • 심사 : 2014.03.05
  • 발행 : 2014.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

A visually servoed paired structured light system (ViSP) was recently proposed as a novel estimation method of the 6-DOF (Degree-Of-Freedom) relative displacement in civil structures. In order to apply the ViSP to massive structures, multiple ViSP modules should be installed in a cascaded manner. In this configuration, the estimation errors are propagated through the ViSP modules. In order to resolve this problem, a displacement estimation error back-propagation (DEEP) method was proposed. However, the DEEP method has some disadvantages: the displacement range of each ViSP module must be constrained and displacement errors are corrected sequentially, and thus the entire estimation errors are not considered concurrently. To address this problem, a pose-graph optimized displacement estimation (PODE) method is proposed in this paper. The PODE method is based on a graph-based optimization technique that considers entire errors at the same time. Moreover, this method does not require any constraints on the movement of the ViSP modules. Simulations and experiments are conducted to validate the performance of the proposed method. The results show that the PODE method reduces the propagation errors in comparison with a previous work.

키워드

과제정보

연구 과제번호 : The Development of Low-Cost Autonomous Navigation Systems for a Robot Vehicle in Urban Environment

연구 과제 주관 기관 : Korea Evaluation Institute of Industrial Technology (KEIT)

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피인용 문헌

  1. Laser pose calibration of ViSP for precise 6-DOF structural displacement monitoring vol.18, pp.4, 2016, https://doi.org/10.12989/sss.2016.18.4.801
  2. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications vol.2016, 2016, https://doi.org/10.1155/2016/7103039
  3. 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
  4. A review on sensors and systems in structural health monitoring: current issues and challenges vol.22, pp.5, 2018, https://doi.org/10.12989/sss.2018.22.5.509