Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting. (March 2020)

• Record Type: Journal Article
• Authors: Xie, Qian; Lu, Dening; Du, Kunpeng; Xu, Jinxuan; Dai, Jiajia; Chen, HongHua; Wang, Jun
• Is Part Of: Computer aided design. Volume 120(2020)
• Publication Date: 2020-03
• Subjects: Raw scanned point cloud -- Local point density -- Geometric model fitting -- Rivet contour; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2019.102805
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Rivet detection is usually the first step for almost all surface and rivet inspection methods in aircraft skins. With 3D laser scanners, one can rapidly obtain the precise 3D information, i.e. point cloud, of the surface and rivets. Subsequently, rivet detection can be converted to a multiple-structure fitting problem from 3D point clouds. However, robust structure fitting from scanned 3D point cloud remains an open problem due to its challenging nature, such as noise and outliers, irregular sampling density and missing scanning. To reduce the fitting variability, this paper presents an automated density-aware multiple-structure fitting algorithm to perform rivet detection based on a 3D point cloud. The key observation is that the local density of points belonging to the rivet contour is relatively higher. We hereby formulate rivet detection as a multiple structure fitting problem with a density-based significance measure. By considering the local distribution characteristics, we first perform adaptive density enhancement on the basic local density. Subsequently, we detect the potential circle hypotheses and thereby extract rivet contours. By performing the mode-seeking algorithm on hypergraphs, all the circle structures can be obtained simultaneously. Overall, the proposed extraction algorithm is able to efficiently and effectively detect rivets from the raw scanned point clouds. We also demonstrate that the proposed algorithm achieves significant superiority over several state-of-the-art model fitting methods on the real scanned point cloud via experimental results. Moreover, we give the application of our algorithm on rivet flush inspection, showing that our method can assist in the rapid measurement of riveting quality. Graphical abstract: Highlights: We propose an automatic rivet extraction algorithm from scanned 3D point clouds. We design a novel point cloud local density computation method. We devise a density-aware hypergraph-based multiple circle fitting algorithm.