Abstract

When a train is running on uneven or curved rails, it generates violent vibrations on the rails. As a result, the light plane of the single-line structured light vision sensor is not vertical, causing errors in rail wear measurements (referred to as vibration errors in this paper). To avoid vibration errors, a novel rail wear measurement method is introduced in this paper, which involves three main steps. First, a multi-line structured light vision sensor (which has at least two linear laser projectors) projects a stripe-shaped light onto the inside of the rail. Second, the central points of the light stripes in the image are extracted quickly, and the three-dimensional profile of the rail is obtained based on the mathematical model of the structured light vision sensor. Then, the obtained rail profile is transformed from the measurement coordinate frame (MCF) to the standard rail coordinate frame (RCF) by taking the three-dimensional profile of the measured rail waist as the datum. Finally, rail wear constraint points are adopted to simplify the location of the rail wear points, and the profile composed of the rail wear points are compared with the standard rail profile in RCF to determine the rail wear. Both real data experiments and simulation experiments show that the vibration errors can be eliminated when the proposed method is used.

© 2014 Optical Society of America

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  1. C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
    [CrossRef]
  2. F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
    [CrossRef]
  3. S. Magnus and D. Magnus, “Rail measurement system,” U.S. patent2009/0073428 A1 (March19, 2009).
  4. W. Y. Chung and P. A. Erie, “Rail vehicle mounted rail measurement system,” U.S. patent2008/0007724 A1 (January10, 2008).
  5. W. R. Jin, X. Q. Zhan, and B. H. Jiang, “Non-contact rail-wear inspecting system based on image understanding,” in Proceedings of International Conference on Mechatronics and Automation (Academic, 2007), pp. 3854–3858.
  6. J. H. Sun, W. H. Wang, and Z. Liu, “Rail wear measurement method based on structured-light vision,” J. Beijing Univ. Aeronaut. Astronaut. 36, 1026–1029 (2010).
  7. Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
    [CrossRef]
  8. http://www.ensco.com/products-services/rail-technologies/track-inspection-systems/rail-surface-wear-condition.htm .
  9. http://www.mermecgroup.com/diagnostics/trackin%20specti-on/60/1/rail-profile.php .
  10. http://beenavision.com/products_trackviewprofile.html .
  11. http://www.railtraksystems.com.au/ .
  12. J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
    [CrossRef]
  13. K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).
  14. C. H. Chen and A. C. Kak, “Model and calibration of a structured light scanner for 3D robot vision,” in Proceedings of IEEE conference on Robotics and Automation (IEEE, 1987), pp. 807–815.
  15. D. Q. Huynh, “Calibration a structured light stripe system: a novel approach,” Int. J. Comput. Vis. 33, 73–86 (1999).
    [CrossRef]
  16. R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82, 35–45 (1960).
    [CrossRef]
  17. R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” J. Basic Eng. 83, 95–107 (1961).
    [CrossRef]
  18. C. Steger, “An unbiased detector of curvilinear structures,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 113–125 (1998).
    [CrossRef]
  19. P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
    [CrossRef]
  20. J. Y. Bouguet, “Camera calibration toolbox for MATLAB,” http://www.vision.caltech.edu/bouguetj/calib_doc .
  21. Z. Y. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).
    [CrossRef]
  22. G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
    [CrossRef]

2013 (1)

J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
[CrossRef]

2011 (1)

Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
[CrossRef]

2010 (2)

J. H. Sun, W. H. Wang, and Z. Liu, “Rail wear measurement method based on structured-light vision,” J. Beijing Univ. Aeronaut. Astronaut. 36, 1026–1029 (2010).

G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
[CrossRef]

2009 (1)

K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).

2007 (1)

F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
[CrossRef]

2000 (2)

C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
[CrossRef]

Z. Y. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).
[CrossRef]

1999 (1)

D. Q. Huynh, “Calibration a structured light stripe system: a novel approach,” Int. J. Comput. Vis. 33, 73–86 (1999).
[CrossRef]

1998 (1)

C. Steger, “An unbiased detector of curvilinear structures,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 113–125 (1998).
[CrossRef]

1992 (1)

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

1961 (1)

R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” J. Basic Eng. 83, 95–107 (1961).
[CrossRef]

1960 (1)

R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82, 35–45 (1960).
[CrossRef]

Alippi, C.

C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
[CrossRef]

Attivissimo, F.

F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
[CrossRef]

Besl, P. J.

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

Bucy, R. S.

R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” J. Basic Eng. 83, 95–107 (1961).
[CrossRef]

Casagrande, E.

C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
[CrossRef]

Chen, C. H.

C. H. Chen and A. C. Kak, “Model and calibration of a structured light scanner for 3D robot vision,” in Proceedings of IEEE conference on Robotics and Automation (IEEE, 1987), pp. 807–815.

Choi, Y. S.

K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).

Chung, W. Y.

W. Y. Chung and P. A. Erie, “Rail vehicle mounted rail measurement system,” U.S. patent2008/0007724 A1 (January10, 2008).

Danese, A.

F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
[CrossRef]

Erie, P. A.

W. Y. Chung and P. A. Erie, “Rail vehicle mounted rail measurement system,” U.S. patent2008/0007724 A1 (January10, 2008).

Giaquinto, N.

F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
[CrossRef]

Huynh, D. Q.

D. Q. Huynh, “Calibration a structured light stripe system: a novel approach,” Int. J. Comput. Vis. 33, 73–86 (1999).
[CrossRef]

Jiang, B. H.

W. R. Jin, X. Q. Zhan, and B. H. Jiang, “Non-contact rail-wear inspecting system based on image understanding,” in Proceedings of International Conference on Mechatronics and Automation (Academic, 2007), pp. 3854–3858.

Jin, W. R.

W. R. Jin, X. Q. Zhan, and B. H. Jiang, “Non-contact rail-wear inspecting system based on image understanding,” in Proceedings of International Conference on Mechatronics and Automation (Academic, 2007), pp. 3854–3858.

Kak, A. C.

C. H. Chen and A. C. Kak, “Model and calibration of a structured light scanner for 3D robot vision,” in Proceedings of IEEE conference on Robotics and Automation (IEEE, 1987), pp. 807–815.

Kalman, R. E.

R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” J. Basic Eng. 83, 95–107 (1961).
[CrossRef]

R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82, 35–45 (1960).
[CrossRef]

Lee, H.

K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).

Liu, Z.

J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
[CrossRef]

Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
[CrossRef]

J. H. Sun, W. H. Wang, and Z. Liu, “Rail wear measurement method based on structured-light vision,” J. Beijing Univ. Aeronaut. Astronaut. 36, 1026–1029 (2010).

G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
[CrossRef]

Magnus, D.

S. Magnus and D. Magnus, “Rail measurement system,” U.S. patent2009/0073428 A1 (March19, 2009).

Magnus, S.

S. Magnus and D. Magnus, “Rail measurement system,” U.S. patent2009/0073428 A1 (March19, 2009).

McKay, N. D.

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

Piuri, V.

C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
[CrossRef]

Rhee, S.

K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).

Scotti, F.

C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
[CrossRef]

Sforza, P.

F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
[CrossRef]

Steger, C.

C. Steger, “An unbiased detector of curvilinear structures,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 113–125 (1998).
[CrossRef]

Sun, J.

G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
[CrossRef]

Sun, J. H.

J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
[CrossRef]

Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
[CrossRef]

J. H. Sun, W. H. Wang, and Z. Liu, “Rail wear measurement method based on structured-light vision,” J. Beijing Univ. Aeronaut. Astronaut. 36, 1026–1029 (2010).

Sung, K.

K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).

Wang, H.

Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
[CrossRef]

Wang, W. H.

J. H. Sun, W. H. Wang, and Z. Liu, “Rail wear measurement method based on structured-light vision,” J. Beijing Univ. Aeronaut. Astronaut. 36, 1026–1029 (2010).

Wei, Z. Z.

G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
[CrossRef]

Zhan, X. Q.

W. R. Jin, X. Q. Zhan, and B. H. Jiang, “Non-contact rail-wear inspecting system based on image understanding,” in Proceedings of International Conference on Mechatronics and Automation (Academic, 2007), pp. 3854–3858.

Zhang, G.

J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
[CrossRef]

Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
[CrossRef]

Zhang, G. J.

G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
[CrossRef]

Zhang, Z. Y.

Z. Y. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).
[CrossRef]

Zhao, Y. T.

J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
[CrossRef]

EEE Trans. Instrum. Meas. (1)

F. Attivissimo, A. Danese, N. Giaquinto, and P. Sforza, “A railway measurement system to evaluate the wheel–rail interaction quality,” EEE Trans. Instrum. Meas. 56, 1583–1589 (2007).
[CrossRef]

IEEE Trans. Instrum. Meas. (1)

C. Alippi, E. Casagrande, F. Scotti, and V. Piuri, “Composite real-time image processing for railways track profile measurement,” IEEE Trans. Instrum. Meas. 49, 559–564 (2000).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

C. Steger, “An unbiased detector of curvilinear structures,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 113–125 (1998).
[CrossRef]

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

Z. Y. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).
[CrossRef]

Int. J. Comput. Vis. (1)

D. Q. Huynh, “Calibration a structured light stripe system: a novel approach,” Int. J. Comput. Vis. 33, 73–86 (1999).
[CrossRef]

J. Basic Eng. (2)

R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82, 35–45 (1960).
[CrossRef]

R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” J. Basic Eng. 83, 95–107 (1961).
[CrossRef]

J. Beijing Univ. Aeronaut. Astronaut. (1)

J. H. Sun, W. H. Wang, and Z. Liu, “Rail wear measurement method based on structured-light vision,” J. Beijing Univ. Aeronaut. Astronaut. 36, 1026–1029 (2010).

Opt. Eng. (1)

G. J. Zhang, Z. Liu, Z. Z. Wei, and J. Sun, “Novel calibration method for a multi-sensor visual measurement system based on structured light,” Opt. Eng. 49, 043602 (2010).
[CrossRef]

Opt. Laser Eng. (1)

Z. Liu, J. H. Sun, H. Wang, and G. Zhang, “Simple and fast rail wear measurement method based on structured light,” Opt. Laser Eng. 49, 1343–1351 (2011).
[CrossRef]

Opt. Laser Technol. (1)

J. H. Sun, Z. Liu, Y. T. Zhao, and G. Zhang, “Motion deviation rectifying method of dynamically measuring rail wear based on multi-line structured-light vision,” Opt. Laser Technol. 50, 25–32 (2013).
[CrossRef]

Weld. J. (1)

K. Sung, H. Lee, Y. S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Weld. J. 88, 79–85 (2009).

Other (9)

C. H. Chen and A. C. Kak, “Model and calibration of a structured light scanner for 3D robot vision,” in Proceedings of IEEE conference on Robotics and Automation (IEEE, 1987), pp. 807–815.

J. Y. Bouguet, “Camera calibration toolbox for MATLAB,” http://www.vision.caltech.edu/bouguetj/calib_doc .

http://www.ensco.com/products-services/rail-technologies/track-inspection-systems/rail-surface-wear-condition.htm .

http://www.mermecgroup.com/diagnostics/trackin%20specti-on/60/1/rail-profile.php .

http://beenavision.com/products_trackviewprofile.html .

http://www.railtraksystems.com.au/ .

S. Magnus and D. Magnus, “Rail measurement system,” U.S. patent2009/0073428 A1 (March19, 2009).

W. Y. Chung and P. A. Erie, “Rail vehicle mounted rail measurement system,” U.S. patent2008/0007724 A1 (January10, 2008).

W. R. Jin, X. Q. Zhan, and B. H. Jiang, “Non-contact rail-wear inspecting system based on image understanding,” in Proceedings of International Conference on Mechatronics and Automation (Academic, 2007), pp. 3854–3858.

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Figures (19)

Fig. 1.
Fig. 1.

Schematic of the measurement results of the rail wear influenced by the light plane.

Fig. 2.
Fig. 2.

Schematic of the measurement system.

Fig. 3.
Fig. 3.

Prediction of the region of the rail waist light stripe in the light stripe image.

Fig. 4.
Fig. 4.

Schematic of the distribution of the large and small circles at the rail waist.

Fig. 5.
Fig. 5.

Distribution diagram of θp.

Fig. 6.
Fig. 6.

Schematic of the iterative optimization process.

Fig. 7.
Fig. 7.

Matching result after the measured rail profile is transformed to RCF by Tm2r.

Fig. 8.
Fig. 8.

Definitions of rail wear and rail wear constraint points.

Fig. 9.
Fig. 9.

Schematic of the positioning of image points corresponding to vertical and horizontal wear points.

Fig. 10.
Fig. 10.

RMS errors of the vertical and horizontal wear at different noise levels.

Fig. 11.
Fig. 11.

RMS error of the vertical and horizontal wear with respect to rotation angle. (a) RMS error of rail wear with rotation around the x axis, and (b) around the y axis.

Fig. 12.
Fig. 12.

Influence of the number of the iterative optimizations on the measurement accuracy of rail wear.

Fig. 13.
Fig. 13.

Schematic of the measurement system in the real data experiment.

Fig. 14.
Fig. 14.

Static experiment results of SLM and MLM.

Fig. 15.
Fig. 15.

Static experiment results of MLM and MLM_S.

Fig. 16.
Fig. 16.

Dynamic experiment results of SLM and MLM when the rail rotates around the x axis.

Fig. 17.
Fig. 17.

Dynamic experiment results of MLM and MLM_S when the rail rotates around the x axis.

Fig. 18.
Fig. 18.

Dynamic experiment results of MLM and SLM when the rail rotates around the y axis.

Fig. 19.
Fig. 19.

Dynamic experiment results of MLM and MLM_S when the rail rotates around y axis.

Tables (3)

Tables Icon

Table 1. Measurement Results for Rail Wear with the Three Methods in the Static Experiment

Tables Icon

Table 2. Measurement Results for Rail Wear with the Three Methods in the Dynamic Experiment around the X Axis

Tables Icon

Table 3. Measurement Results for Rail Wear with the Three Methods in the Dynamic Experiment around the Y Axis

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

ρp˜=A[I0]qm,
A=[fxγu00fyv0001]
axm+bym+czm+d=0,
{ρp˜=A[I0]qm[abcd]qm=0.
P=dRP+dt,
Tm2r=(dRdt01)Tm2r.
πr=Tm2rTπm.
{ρ1pa=A[Rm2rtm2r]maρ2pb=A[Rm2rtm2r]mbρ3pc=A[Rm2rtm2r]mcρ4pd=A[Rm2rtm2r]md.
{WV=yVyVbWH=xHxHb.

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