Paper Info
Title
Extracting Sectional Contours From Scanned Point Clouds Via Adaptive Surface Projection
Abstract
This paper presents a new and fully automatic method to extract cross-sectional contour profiles of a physical object from the point cloud data scanned from its surface. Correctly extracting the sectional contours is of particular importance in the quality inspection of airfoil blades as the tolerances specified on a manufactured aero-engine blade are generally imposed at specific blade sections. The collected point cloud via 3D laser scanning is, however, distributed all over the blade surface rather than at the desired specific sections. In fact, no point in the point cloud is located exactly on the sectional planes. The desired sectional data have to be extracted from the nearby data points. If the underlying smooth surface geometry of the point cloud in the vicinity of a nearby data point can be approximated by a mathematical function, the approximated local surface formulation can be used to project the nearby point onto the desired sectional plane along a curvilinear trajectory. This is achieved in this work by fitting a local quadric surface to the neighbouring points of the point of interest. A systematic approach to establish a balanced set of neighbouring points is employed to avoid bias in fitting the local quadric surface as well as to guide the selection of points to be projected onto the sectional plane. The projected points are then used to construct the desired sectional contour profile. Implementation results have demonstrated the superior performance of the proposed fully automatic method in comparison with the existing methods.
Year
DOI
Venue
2017
10.1080/00207543.2016.1262565
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Keywords
DocType
Volume
blade inspection, scanned point cloud, sectional contour, local surface fitting, curvilinear projection
Journal
55
Issue
ISSN
Citations 
15
0020-7543
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Farbod Khameneifar100.68
Hsi-yung Feng215215.49