Calibration of five-axis motion platform based on monocular vision

Lü, Qiang; Zhou, Haibo; Li, Zhiqiang; Ju, Xia; Tan, Shuaixia; Duan, Ji’an

doi:10.1007/s00170-021-07402-x

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…The main contribution of this research is to propose an online kinematic calibration method for a five-axis motion platform, which can still recognize effectively when the five axes are moving simultaneously. It is worth noting that this research continues the author's previous work [26,27]. A calibration method for geometric errors on the rotating axis is proposed in [26].…”

Section: Introductionsupporting

confidence: 53%

Monocular vision-based online kinematic calibration method for five-axis motion platform

Tang,

Zhou,

Jiang

2023

Meas. Sci. Technol.

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Aiming at the problems of low kinematics accuracy and a highly complex kinematic calibration of the five-axis motion platform, this paper proposes an online kinematic calibration method for simultaneous five-axis motion. First, the ArUco markers are used in vision systems for large stroke detection, and kinematics models of the five-axis motion platform are established through the screw theory. Following, this paper proposes an online kinematic parameter identification method for simultaneous five-axis motion, using a monocular camera as measurement tool. Furthermore, the stability and effectiveness of the identification algorithm are verified by simulation and experiment. Specifically, a process trajectory commonly used to carry out experiments, verifying the influence of the scheme on the kinematic accuracy. Experimental results show that the kinematic calibration method reduce the average position error of the five-axis motion platform by 88.59% and the average direction error by 84.54%. Therefore, the experiment proves that the kinematic calibration method can significantly improve the kinematic accuracy of the five-axis motion platform and it verifies the applicability and effectiveness of the proposed scheme.

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Section: Introductionsupporting

confidence: 53%

Monocular vision-based online kinematic calibration method for five-axis motion platform

Tang,

Zhou,

Jiang

2023

Meas. Sci. Technol.

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“…The extrinsic parameters are the pose matrix of the calibration plate expressed in the camera coordinates system. By detecting the corners of the target and extracting its sub-pixel coordinates, the pose of the target, relative to the camera, can be calculated by the PNP algorithm [36].…”

Section: Error Identification Resultsmentioning

confidence: 99%

“…When axes A and C are in the initial state, g w b (0, 0) = g w b (0). When PIGEs are used to represent the screw of the actual axis direction, a relationship between the unit motion screw of the actual axis and the ideal axis exists, as shown in equation (36). The aforementioned indicates that the initial pose matrix g w b (0) already contains PIGEs(A) and PIGEs(C). )…”

Section: Discussion Of Error Modeling Methodsmentioning

confidence: 99%

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Vision-based measurement for decoupling identification of geometric errors of rotating axes for five-axis platform

Yin¹,

Zhou²,

Ju³

et al. 2022

Meas. Sci. Technol.

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In this paper, a method for identifying and decoupling geometric errors of rotation axes using vision measurement is proposed. Based on screw theory and exponential product formula, identification equations of position-dependent geometric errors (PDGEs) and position-independent geometric errors (PIGEs) of the rotation axes are established. The mapping relationships between the error twist and geometric errors are established. The error model provides the coupling mechanism of PDGEs and PIGEs. Furthermore, a progressive decoupling method is proposed to separate PDGEs and PIGEs without additional assumptions. The pose parameters, required for solving the identification equations, are obtained by visual measurement. Then, the error terms of PIGEs and PDGEs are determined. Lastly, the error calibration of the rotation axes is investigated, thus providing an average rotary table orientation error reduction of 28.1% compared to the situation before calibration.

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“…Stereo vision, a type of 3D imaging system, captures information simultaneously from different views, containing 3D object information. Stereo vision styles include monocular vision [1][2][3][4], binocular vision [5][6][7][8][9], and multiview stereo vision [10][11][12], the latter being a more complex binocular variant.…”

Section: Introductionmentioning

confidence: 99%

Increased plane identification precision with stereo identification

Zhao

2023

Robotica

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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%.

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Calibration of five-axis motion platform based on monocular vision

Cited by 9 publications

References 28 publications

Monocular vision-based online kinematic calibration method for five-axis motion platform

Monocular vision-based online kinematic calibration method for five-axis motion platform

Vision-based measurement for decoupling identification of geometric errors of rotating axes for five-axis platform

Increased plane identification precision with stereo identification

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