Automated precision measurement of surface profile in CAD-directed inspection

Menq, Chia-Hsiang; Yau, Hong-Tzong; Lai, Guan-Wen

doi:10.1109/70.134279

Cited by 182 publications

(60 citation statements)

References 5 publications

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“…In the literature, refinement usually depends on application of the well-known iterative closest point (ICP) algorithm [6,14,42]. ICP starts from an initial transformation estimate, and uses this to map points from one scan (the ''moving'' scan) onto the second scan (the ''fixed'' scan) in order to establish temporary correspondences between points in the two scans.…”

Section: Related Workmentioning

confidence: 99%

Registration of combined range–intensity scans: Initialization through verification

Smith

King

Stewart

et al. 2008

Computer Vision and Image Understanding

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This paper presents an automatic registration system for aligning combined range-intensity scan pairs. The overall approach is designed to handle several challenges including extensive structural changes, large viewpoint differences, repetitive structure, illumination differences, and flat regions. The technique is split into three stages: initialization, refinement, and verification. During initialization, intensity keypoints are backprojected into the scans and matched to form candidate transformations, each based on a single match. We explore methods of improving this image-based matching using the range data. For refinement, we extend the Dual-Bootstrap ICP algorithm for alignment of range data and introduce novel geometric constraints formed by backprojected image-based edgel features. The verification stage determines if a refined transformation is correct. We treat verification as a classification problem based on accuracy, stability, and a novel boundary alignment measure. Experiments with 14 scan pairs show both the overall effectiveness of the algorithm and the importance of its component techniques.

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Section: Related Workmentioning

confidence: 99%

Registration of combined range–intensity scans: Initialization through verification

Smith

King

Stewart

et al. 2008

Computer Vision and Image Understanding

View full text Add to dashboard Cite

show abstract

“…Being particularly legible and effective, this method or its improved variants [2][3][4][5][6][7] are widely used in localization problems. Based on a similar idea, Menq et al [8,9] developed an optimal match scheme that aligns the measured data with the design surface for the precision measurement of the surface profile. To improve the computational efficiency of the transformation matrix, a modified algorithm is then proposed based on the technique of pseudoinverse.…”

Section: Introductionmentioning

confidence: 99%

“…Normally, it is treated as solving a non-linear optimization problem. Various optimization methods such as the Newton method and NewtonRaphson method are usually used to solve it [8,10,18,19]. When a good initial value is available, the result of this iterative approach is of high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal localization of complex surfaces in CAD-based inspection

Liu

Sun

2008

Front. Mech. Eng. China

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Complex surface inspection requires the optimal localization of the measured surface related to the design surface so that the two surfaces can be compared in a common coordinate frame. This paper presents a new technique for solving the localization problem. The basic approach consists of two steps: 1) rough localization of the measured points to the design surface based on curvature features, which can produce a good initial estimate for the optimal localization; 2) fine localization based on the least-square principle so that the deviation between the measured surface and the design surface is minimized. To efficiently compute the closest points on the design surface of the measured points, a novel method is proposed. Since this approach does not involve an iterative process of solving non-linear equations for the closest points, it is more convenient and robust. The typical complex surface is used to test the developed algorithm. Analysis and comparison of experimental results demonstrate the validity and applicability of the algorithm.

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“…In industrial automation, a workpiece is typically localized by finding the optimal registration of some measured points onto a given CAD model [19], [31], [33]. The general scheme iteratively improves on a transformation in order to minimize some least-squares error function and also on the registration of the measured data points.…”

Section: Introductionmentioning

confidence: 99%

Localization of curved parts through continual touch

Jia

2005

IEEE Trans. Robot.

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Abstract-We describe a simple system that localizes two-dimensional curved shapes through touch sensing, offering computational and experimental studies. The idea lies in determining the placement of a manipulator on a curved object during some special motion-rolling. A geometric algorithm is introduced to locate the boundary segment traced out by their contact using tactile data. Both completeness and local convergence have been established. The algorithm is asymptotically as efficient as evaluating the object's perimeter through numerical integration. For implementation, a two-axis force/torque sensor has been designed to realize contact sensing. Functioning like a "wrist," the sensor is calibrated over the ratio between the bending and twisting moments, eliminating the need for known weights. A simple geometry-based control strategy is devised to implement the rolling motion. Experiments have been conducted with an Adept Cobra 600 manipulator.

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Automated precision measurement of surface profile in CAD-directed inspection

Cited by 182 publications

References 5 publications

Registration of combined range–intensity scans: Initialization through verification

Registration of combined range–intensity scans: Initialization through verification

Optimal localization of complex surfaces in CAD-based inspection

Localization of curved parts through continual touch

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