A non-contact gear measurement method based on laser vision

Song, Limei; Qin, Ming-cui; Li, Zongyan; Yang, Yan-gang; Li, Dapeng

doi:10.1007/s11801-014-4047-1

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The above traditional image matching methods based on SIFT [11][12] , SURF [13][14][15] , BRIEF [16][17] , and ORB [18][19][20] all rely on manually designed descriptors. Although they have certain effects, considering the on-site environment of the intelligent sorting robot for coal gangue, there are still some shortcomings.…”

Section: Related Workmentioning

confidence: 99%

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Ye,

Hongwei,

Peng

et al. 2024

Preprint

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The coal gangue sorting robot may encounter variations in the pose of the target coal gangue due to belt slippage, deviation, and speed fluctuations, leading to failed or missed grasping attempts during the sorting process of coal gangue. In response to this issue, we propose a special two-stage network coal gangue image fast matching method to re-obtain the target coal gangue pose information, further improving the grasping accuracy and efficiency of the robot. In the first stage, we use SuperPoint to enhance the scene adaptability and credibility of feature point extraction. The improved Multi-scale Retinex with Color Restoration enhancement algorithm is used to further enhance the ability of Superpoint to detect feature points. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. Integrating the Progressive Sample Consensus algorithm to further eliminate erroneous feature matching point pairs and improve the accuracy of image matching. We conducted matching experiments of coal gangue under different object distances, scales, and rotation angles using various methods on the double-manipulator truss-type coal gangue sorting robot experimental platform independently developed by our team. The results showed that the matching rate of the proposed method was 98.2%, with a matching time of 84.6ms. It has the characteristics of a high matching rate, good real-time performance, and strong robustness, and can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

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Section: Related Workmentioning

confidence: 99%

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Ye,

Hongwei,

Peng

et al. 2024

Preprint

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show abstract

“…The experimental results showed that the size measurement error is less than 0.04 mm. Song et al 7 studied a binocular vision measurement method based on improved FAST and BRIEF. They matched the feature points through FAST and BRIEF, mapped the feature points to the world coordinate system based on binocular vision, and calculated the size according to the feature points in the world coordinate system.…”

Section: Introductionmentioning

confidence: 99%

Visual measurement of the bearing diameter based on the homography matrix and partial area effect

Jiang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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A measurement method based on the homography matrix and partial area effect is studied to improve the precision of the visual measurement of the bearing size. Firstly, the camera is calibrated to obtain its distortion coefficient, and the image is corrected for distortion. Secondly, the relationship between the pixel plane and the bearing imaging plane is determined by solving the homography matrix with a chessboard. Finally, the sub-pixel edge contour of the bearing is obtained through the improved partial area effect, and the pixel coordinates are mapped to the world coordinates according to the homography matrix. The feature size is obtained by fitting the mapping points. Experimental results showed that the transformation of the homography matrix is more accurate than the pixel equivalent in the process of converting the pixel size to the actual size. The improved partial area effect sub-pixel edge detection results are better than the partial area effect and Zernike moment. The measurement error for the bearing outer diameter is less than 0.05 mm and is within 0.5 pixel.

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“…Given the problems above, scholars have conducted a series of studies and explorations to improve the accuracy of optical measurements of gears. Song et al [3] developed a laser vision-based non-contact gear measurement system and proposed a laser vision precision measurement method (LVPMM) to ensure accuracy. The experimental results indicated that gear measurement uncertainty was 2.1 μm and the maximum helical deviation was 35 μm, which meets industrial class II measurement requirements (and below) for standard gears.…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction of a Digital Twin Model for a 3D Measurement System of Gears

Li,

Guo,

Guo

et al. 2023

IEEE Access

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As an advanced metrology technology, three-dimensional (3D) gear measurement using linear laser techniques is susceptible to the influence of multiple coupling factors. Consequently, there exists an urgent need for new principles to effectively decouple the effects of each factor. Digital twins realize the connection between physical and virtual spaces. By fusing multi-source data from the existing system, a highfidelity digital model is established to realize the simulation of the gear measurement process in this paper. Moreover, simulation of the light reflections of tooth flanks and the dynamic error coupling process can effectively clarify the coupling mechanism of measurement errors. This paper presents a method of constructing a digital twin model of a 3D measurement system for gears and realizes the expression of the actual measurement processing. It provides the foundation for subsequently studying the reflection characteristics of the tooth flank at different incident angles and searching for the optimum incident attitude of the line structured light sensor.INDEX TERMS digital twin, model construction, multi-source data, gear measurement.

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A non-contact gear measurement method based on laser vision

Cited by 4 publications

References 11 publications

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Visual measurement of the bearing diameter based on the homography matrix and partial area effect

Research on the Construction of a Digital Twin Model for a 3D Measurement System of Gears

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