Part-in-whole point cloud registration for aircraft partial scan automated localization. (August 2021)

• Record Type: Journal Article
• Authors: Xie, Qian; Zhang, Yiming; Cao, Xuanming; Xu, Yabin; Lu, Dening; Chen, HongHua; Wang, Jun
• Is Part Of: Computer aided design. Volume 137(2021)
• Publication Date: 2021-08
• Subjects: Raw scanned point cloud -- Seam structure -- Part-in-whole registration -- Aircraft skin; 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.2021.103042
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Skin deformation measurement is quite important for the aerodynamic performance of aircrafts. To achieve this, 3D scanning is usually adopted to capture the 3D point cloud, which reflects the geometric information of partial skin surface. The scanned point cloud is then compared with the designed CAD model to quantify the shape deformation. However, accurate localization of the partial scanned point cloud, which is the prerequisite for the shape comparison, still remains an open problem. This can be formulated as a part-in-whole registration problem in the free-form surface, which is of significant difficulty due to the featurelessness and local similarity problems on the aircraft skin surface. In this paper, we propose a "coarse-to-fine" registration framework to address these two challenges. First, a Multi-Descriptor Voting (MDV) scheme is presented to roughly locate the partial scanned point cloud in the whole aircraft skin model. A voting mechanism using multiple 3D descriptors leads to the high possibility of correct localization under the featurelessness situation on the free-form surface. Then, we observed in the aircraft skin that there are seam structures which can assist in localization, we accordingly design a Seam Structure Aware ICP (SSA-ICP) for fine registration to eliminate the localization ambiguity in local regions, based on the detected seam points. The proposed algorithm is implemented using Point Cloud Library (PCL), and the results show that the proposed MDV and SSA-ICP achieve promising performance on both synthetic and real scanned point clouds. Highlights: A part-in-whole registration framework for partial aircraft scan localization. A multi-descriptor voting-based shape retrieval strategy for the coarse localization. A seam structure-aware ICP for fine registration in aircraft local regions.