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Publishing Language: Chinese

Integrated calibration of internal visual sensor parameters based on combined laser structured lights

Chuanhui ZHUZhiming ZHU( )Zhijie KETianyi ZHANG
Key Laboratory for Advanced Materials Processing Technology of Ministry of Education, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
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Abstract

Visual sensing is a key technology for online detection of welding groove size parameters, and welding torch positions and postures in intelligent welding systems. Accurate visual sensing requires accurate calibration of the internal visual sensor parameters. This paper describes an integrated calibration method for the internal parameters of a visual sensor based on combined laser structured lights. The method is based on an ordinary checkerboard calibration board with the calibration system extracting the centerline of the laser line in the image using the skeleton thinning method and Hough line detection. The system then determines the three-dimensional coordinates in the camera coordinate system of the points on the laser center line of the calibration board to fit the parameters in the laser structured light plane equation. This integrated calibration method improves the calibration accuracy, efficiency and convenience. Tests measuring the welding groove size on a flat workpiece gave mean and repeated measurement errors of the welding groove size of not more than 0.04 mm, which verifies that the sensor calibration accuracy meets the needs for welding groove size measurements.

CLC number: TP212.9 Document code: A Article ID: 1000-0054(2022)09-1516-08

References

[1]

CHEN S B, LÜ N. Research evolution on intelligentized technologies for arc welding process[J]. Journal of Manufacturing Processes, 2014, 16(1): 109-122.

[2]

LIN T, CHEN H B, LI W H, et al. Intelligent methodology for sensing, modeling, and control of weld penetration in robotic welding system[J]. Industrial Robot, 2009, 36(6): 585-593.

[3]

BI C, HAO X, ZHOU P. Research on calibration method of the cross structured light sensor[J]. Journal of Astronautic Metrology and Measurement, 2020, 40(6): 69-75. (in Chinese)

[4]

ZHOU J B, LI Y H, QIN Z Y, et al. Calibration of line structured light sensor based on reference target[J]. Acta Optica Sinica, 2019, 39(4): 0412005. (in Chinese)

[5]

ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.

[6]

WEI Z Z, ZHANG G J, XU Y. Calibration approach for structured-lighted-stripe vision sensor[J]. Chinese Journal of Mechanical Engineering, 2005, 41(2): 210-214. (in Chinese)

[7]

HAN J D, LÜ N G, DONG M L, et al. Fast method to calibrate structure parameters of line structured light vision sersor[J]. Optics and Precision Engineering, 2009(5): 958-963. (in Chinese)

[8]

LIU Z, ZHANG G J, WEI Z Z, et al. An accurate calibration method for line structured light vision sensor[J]. Acta Optica Sinica, 2009, 29(11): 3124-3128. (in Chinese)

[9]

WANG J Q, DUAN F J, BO E, et al. Calibration of line structured light scanning sensor structure parameter integration[J]. Chinese Journal of Sensors and Actuators, 2014, 27(9): 1196-1201. (in Chinese)

[10]

ZOU Y Y, LI P F, ZUO K Z. Field calibration method for three-line structured light vision sensor[J]. Infrared and Laser Engineering, 2018, 47(6): 0617002. (in Chinese)

[11]

LÜ Z H, ZHANG Z Y. Build 3D scanner system based on binocular stereo vision[J]. Journal of Computers, 2012, 7(2): 399-404.

[12]

STEGER C. An unbiased detector of curvilinear structures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(2): 113-125.

[13]
ZHANG T Y. Detection and feedback control system of welding torch position and posture based on vision and gravity sensing[D]. Beijing: Tsinghua University, 2021. (in Chinese)
[14]

ZOU Y Y, ZHAO M Y, ZHANG L, et al. Error analysis and structural analysis of structured-light visual sensor for seam tracking[J]. Chinese Journal of Scientific Instrument, 2008, 29(12): 2605-2610. (in Chinese)

[15]

LIU N S, GUO C R, LIU M Y, et al. The internal layout experiment research of structural light visual sensor used in crawling arc welding robot[J]. Jiangxi Science, 2005, 23(4): 325-327, 355. (in Chinese)

[16]

PAN Y, ZHANG W, ZHAO Y, et al. On-site calibration method for the parameters of light sensor with line structure[J]. Process Automation Instrumentation, 2017, 38(9): 48-52. (in Chinese)

[17]
CHEN W, SUI L C, XU Z C, et al. Improved Zhang-Suen thinning algorithm in binary line drawing applications[C]// 2012 International Conference on Systems and Informatics (ICSAI2012). Yantai, China: IEEE, 2012: 1947-1950.
Journal of Tsinghua University (Science and Technology)
Pages 1516-1523
Cite this article:
ZHU C, ZHU Z, KE Z, et al. Integrated calibration of internal visual sensor parameters based on combined laser structured lights. Journal of Tsinghua University (Science and Technology), 2022, 62(9): 1516-1523. https://doi.org/10.16511/j.cnki.qhdxxb.2022.26.006

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Received: 23 September 2021
Published: 15 September 2022
© Journal of Tsinghua University (Science and Technology). All rights reserved.
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