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A one-step calibration method without redundant parameters for a laser stripe sensor

Published online by Cambridge University Press:  08 January 2024

Yang Mao Open the ORCID record for Yang Mao [Opens in a new window] ,
Yu He ,
Chengyi Yu ,
Honghui Zhang ,
Ke Zhang  and
Xiaojun Sun
Yang Mao
Affiliation:
School of Mechanical and Automation Engineering, Shanghai Institute of Technology, Shanghai, China
Yu He
Affiliation:
School of Mechanical and Automation Engineering, Shanghai Institute of Technology, Shanghai, China
Chengyi Yu
Affiliation:
Shanghai Satellite Equipment Research Institute, Shanghai, China
Honghui Zhang
Affiliation:
Shanghai Platform for Smart Manufacturing, Shanghai, China
Ke Zhang*
Affiliation:
School of Mechanical and Automation Engineering, Shanghai Institute of Technology, Shanghai, China
Xiaojun Sun
Affiliation:
Shanghai Waigaoqiao Shipbuilding Co., Ltd., Shanghai, China
*
Corresponding author: Ke Zhang; Email: zkwy2004@126.com
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Abstract

A laser stripe sensor has two kinds of calibration methods. One is based on the homography model between the laser stripe plane and the image plane, which is called the one-step calibration method. The other is based on the simple triangular method, which is named as the two-step calibration method. However, the geometrical meaning of each element in the one-step calibration method is not clear as that in the two-step calibration method. A novel mathematical derivation is presented to reveal the geometrical meaning of each parameter in the one-step calibration method, and then the comparative study of the one-step calibration method and the two-step calibration method is completed and the intrinsic relationship is derived. What is more, a one-step calibration method is proposed with 7 independent parameters rather than 11 independent parameters. Experiments are conducted to verify the accuracy and robust of the proposed calibration method.

Keywords

calibrationlaser stripe sensorone-step calibration methodtwo-step calibration method
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e1
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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