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Increased plane identification precision with stereo identification

Published online by Cambridge University Press:  19 June 2023

Junjie Ji  and
Jing-Shan Zhao Open the ORCID record for Jing-Shan Zhao [Opens in a new window]
Junjie Ji
Affiliation:
State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, P. R. China
Jing-Shan Zhao*
Affiliation:
State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, P. R. China
*
Corresponding author: Jing-Shan Zhao; Email: jingshanzhao@mail.tsinghua.edu.cn
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Abstract

Stereo vision allows machines to perceive their surroundings, with plane identification serving as a crucial aspect of perception. The accuracy of identification constrains the applicability of stereo systems. Some stereo vision cameras are cost-effective, compact, and user-friendly, resulting in widespread use in engineering applications. However, identification errors limit their effectiveness in quantitative scenarios. While certain calibration methods enhance identification accuracy using camera distortion models, they rely on specific models tailored to a camera’s unique structure. This article presents a calibration method that is not dependent on any particular distortion model, capable of correcting plane position and orientation identified by any algorithm, provided that the identification error is biased. A high-precision mechanical calibration platform is designed to acquire accurate calibration data while using the same detected material in real measurement scenarios. Experimental comparisons confirm the efficacy of plane pose correction on PCL-RANSAC, with the average relative error of distance reduced by 5.4 times and the average absolute error of angle decreasing by 41.2%.

Keywords

stereo cameraplane parameters identificationcalibrationregion of interests

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 9 , September 2023 , pp. 2789 - 2808
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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