An approach for extracting curve profiles based on scanned point cloud. (January 2020)

• Record Type: Journal Article
• Authors: Fan, Jingjing; Ma, Liqun; Sun, Anbin; Zou, Zhi
• Is Part Of: Measurement. Volume 149(2020)
• Publication Date: 2020-01
• Subjects: Curve profile -- Section line -- Pattern vector -- Point extraction -- Point cloud data; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107023
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An approach is proposed to identify curve points on three kinds of section lines. Pattern vectors are constructed to represent geometric feature of any section line. Slice set is set to obtain accurate section-line points from scanned point cloud. Sequential searching method identifies feature points on fold-line section lines. Barycenter calculation method identifies points on small-arc ridge section lines. Abstract: To control product quality, it is essential to conduct surface detection and curve detection. The existing approaches are sufficiently developed in detecting surfaces while are limited in detecting curves. It is essential to extract the curve profiles from the product at first. An approach of extracting curve profiles from scanned point cloud is proposed. Firstly, a slice set is constructed to extract section line points from scanned point cloud. Secondly, based on normal angle of adjacent points, three pattern vectors are defined to distinguish fold-line section line, small-arc section line and large-arc section line. Thirdly, three methods are proposed to identify the curve-profile points on the three kinds of section lines. Qualification experiments are conducted on seven circle profiles and a rectangular profile. The results show that extracted curve-profile points deviate from reference data within ±0.1 mm with standard deviations smaller than 0.07 mm.