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使用基于特征的三维全景配准对小内表面轮廓进行三维测量。

Three-dimensional measurement of small inner surface profiles using feature-based 3-D panoramic registration.

作者信息

Gong Yuanzheng, Seibel Eric J

机构信息

University of Washington, Mechanical Engineering Department, Seattle, Washington, United States.

出版信息

Opt Eng. 2017 Jan;56(1). doi: 10.1117/1.OE.56.1.014108. Epub 2017 Jan 30.

Abstract

Rapid development in the performance of sophisticated optical components, digital image sensors, and computer abilities along with decreasing costs has enabled three-dimensional (3-D) optical measurement to replace more traditional methods in manufacturing and quality control. The advantages of 3-D optical measurement, such as noncontact, high accuracy, rapid operation, and the ability for automation, are extremely valuable for inline manufacturing. However, most of the current optical approaches are eligible for exterior instead of internal surfaces of machined parts. A 3-D optical measurement approach is proposed based on machine vision for the 3-D profile measurement of tiny complex internal surfaces, such as internally threaded holes. To capture the full topographic extent (peak to valley) of threads, a side-view commercial rigid scope is used to collect images at known camera positions and orientations. A 3-D point cloud is generated with multiview stereo vision using linear motion of the test piece, which is repeated by a rotation to form additional point clouds. Registration of these point clouds into a complete reconstruction uses a proposed automated feature-based 3-D registration algorithm. The resulting 3-D reconstruction is compared with x-ray computed tomography to validate the feasibility of our proposed method for future robotically driven industrial 3-D inspection.

摘要

随着精密光学元件、数字图像传感器性能的快速发展以及计算机能力的提升,同时成本不断降低,三维(3-D)光学测量已能够在制造和质量控制领域取代更为传统的方法。三维光学测量具有非接触、高精度、操作快速以及可自动化等优点,对于在线制造极为重要。然而,当前大多数光学方法适用于加工零件的外表面而非内表面。本文提出一种基于机器视觉的三维光学测量方法,用于微小复杂内表面(如内螺纹孔)的三维轮廓测量。为获取螺纹的完整形貌范围(峰谷),使用侧视商用刚性内窥镜在已知的相机位置和方向采集图像。利用试件的直线运动通过多视图立体视觉生成三维点云,再通过旋转重复该过程以形成额外的点云。将这些点云配准成完整的重建模型采用了一种基于特征的自动三维配准算法。将所得的三维重建结果与X射线计算机断层扫描进行比较,以验证我们所提出方法在未来机器人驱动的工业三维检测中的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be35/5341795/4a43ea000c50/nihms849099f1.jpg

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