Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2013.09.03
  • Accepted : 2014.03.05
  • Published : 2014.11.25
⟨ Previous Next ⟩

Abstract

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.

Keywords

Acknowledgement

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

Supported by : Korea Evaluation Institute of Industrial Technology (KEIT)

References

  1. Balageas, D., Fritzen, C.P. and Guemes, A. (eds) (2006), Structural Health Monitoring, New Jersey: John Wiley & Sons, Inc.
  2. Casciati, F. and Fuggini, C. (2011), "Monitoring a steel building using GPS sensors", Smart Struct. Syst., 7(5), 349-363. https://doi.org/10.12989/sss.2011.7.5.349
  3. Chang, C.C. and Xiao, X.H. (2009), "Fusion of vision-based displacement and acceleration using Kalman filter", Proceedings of the 5th Int. Workshop on Advanced Smart Materials and Smart Structures Technology, Boston.
  4. Dellaert, F. and Kaess, M. (2006), "Square root SAM: simultaneous localization and mapping via square root information smoothing", Int. J. Robot. Res., 25(12), 1181-1203. https://doi.org/10.1177/0278364906072768
  5. Grisetti, G., Kummerle, R., Stachniss, C. and Burgard, W. (2010), "A tutorial on graph-based SLAM", IEEE Intell. Transport. Syst. Mag., 2(4), 31-43.
  6. Jeon, H., Bang, Y. and Myung, H. (2011), "A paired visual servoing system for 6-DOF displacement measurement of structures", Smart Mater. Struct., 20(4), 45019-45034. https://doi.org/10.1088/0964-1726/20/4/045019
  7. Jeon, H., Shin, J.U. and Myung, H. (2012), "Incremental displacement estimation of structures using paired structured light", Smart Struct. Syst., 9(3), 273-286. https://doi.org/10.12989/sss.2012.9.3.273
  8. Jeon, H., Shin, J.U. and Myung, H. (2013), "The displacement estimation error back-propagation (DEEP) method for a multiple structural displacement monitoring system", Meas. Sci. Technol., 24(4), 045104. https://doi.org/10.1088/0957-0233/24/4/045104
  9. Ji, Y.F. and Chang, C.C. (2008), "Nontarget stereo vision technique for spatiotemporal response measurement of line-like structures", J. Eng. Mech. - ASCE, 134(6), 466-474. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:6(466)
  10. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J. and Dellaert, F. (2012), "iSAM2: Incremental smoothing and mapping using the Bayes tree", Int. J. Robot. Res., 31(2), 216-235. https://doi.org/10.1177/0278364911430419
  11. Kaess, M., Ranganathan, A. and Dellaert, F. (2008), "iSAM: Incremental smoothing and mapping", IEEE T. Robot., 24(6), 1365-1378. https://doi.org/10.1109/TRO.2008.2006706
  12. Lee, D., Kim, H. and Myung, H. (2012a), "2D image feature-based real-time RGB-D 3D SLAM", Proc. of Robot Intelligence Technology and Applications 2012, 485-492.
  13. Lee, D., Kim, H. and Myung, H. (2012b), "GPU-based real-time RGB-D 3D", Proc. of 2012 9th Int. Conf. on Ubiquitous Robots and Ambient Intelligence (URAI), 26-28.
  14. Lee, J.J., Ho, H.N. and Lee, J.H. (2012c), "A vision-based dynamic rotational angle measurement system for large civil structures", Sensors, 12(6), 7326-7336. https://doi.org/10.3390/s120607326
  15. Lee, J.J. and Shinozuka, M. (2006), "Real-time displacement measurement of a flexible bridge using digital image processing techniques", Exp. Mech., 46(1), 105-114. https://doi.org/10.1007/s11340-006-6124-2
  16. Leith, J.G., Thompson, A. and Sloan, T.D. (1989), "A novel dynamic deflection measurement system for large structure", Proceedings of the 4th Int. Conf. on Civil and Structural Engineering Computing, London.
  17. Lu, F. and Milios, E. (1997), "Globally consistent range scan alignment for environment mapping", Auton. Robot., 4(4), 333-349. https://doi.org/10.1023/A:1008854305733
  18. Marecos, J., Castanheira, M. and Trigo, J. (1969), "Field observation of Tagus river suspension bridge", J. Struct. Div.- ASCE, 95(4), 555-583.
  19. Myung, H., Jung, J. and Jeon, H. (2012), "Robotic SHM and model-based positioning system for monitoring and construction automation", Adv. Struct. Eng., 15(6), 943-954. https://doi.org/10.1260/1369-4332.15.6.943
  20. Myung, H., Lee, S.M. and Lee, B.J. (2011), "Paired structured light for structural health monitoring robot system", Struct. Health Monit., 10(1), 49-64. https://doi.org/10.1177/1475921710365413
  21. Nassif, H.H., Gindy, M. and Davis, J. (2005), "Comparison of laser doppler vibrometer with contact sensors for monitoring bridge deflection and vibration", NDT & E Int., 38(3), 213-218. https://doi.org/10.1016/j.ndteint.2004.06.012
  22. Ni, Y.Q., Wong, K.Y. and Xia, Y. (2011), "Health checks through land mark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. https://doi.org/10.1260/1369-4332.14.1.103
  23. Olaszek, P. (1999), "Investigation of the dynamic characteristic of bridge structures using a computer vision method", Measurement, 25(3), 227-236. https://doi.org/10.1016/S0263-2241(99)00006-8
  24. Olson, E., Leonard, J. and Teller, S. (2006), "Fast iterative alignment of pose graphs with poor initial estimates", Proceedings of the 2006 IEEE Int. Conf. on Robotics and Automation (ICRA).
  25. Park, J.W., Lee, J.J., Jung, H.J. and Myung, H. (2010), "Vision-based displacement measurement method for high-rise building structures using partitioning approach", NDT & E Int., 43(7), 642-647. https://doi.org/10.1016/j.ndteint.2010.06.009
  26. Park, K.T., Kim, S.H., Park, H.S. and Lee, K.W. (2005), "The determination of bridge displacement using measured acceleration", Eng. Struct., 27(3), 371-378. https://doi.org/10.1016/j.engstruct.2004.10.013
  27. Psimoulis, P., Pytharouli, S., Karambalis, D. and Stiros, S. (2008), "Potential of global positioning system (GPS) to measure frequencies of oscillations of engineering structures", J. Sound Vib., 318(3), 606-623. https://doi.org/10.1016/j.jsv.2008.04.036
  28. Sciavicco L. and Siciliano B. (1988), "A solution algorithm to the inverse kinematic problem for redundant manipulator", IEEE J. Robot. Autom., 4(4), 403-410. https://doi.org/10.1109/56.804
  29. Wahbeh, A.M., Caffrey, J.P. and Masri, S.F. (2003), "A vision-based approach for the direct measurement of displacements in vibrating systems", Smart Mater. Struct., 12(5), 785-794. https://doi.org/10.1088/0964-1726/12/5/016
  30. Xu, Y.L. and Chan, W.S. (2009), "Wind structural monitoring of long span cable-supported bridges with GPS", Proceedings of the 7th Asia-Pacific Conf. on Wind Engineering, Taipei.

Cited by

  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