Raw Scanned Point Cloud Registration with Repetition for Aircraft Fuel Tank Inspection. (March 2022)

• Record Type: Journal Article
• Authors: Cao, Xuanming; Gong, Xiaoxi; Xie, Qian; Huang, Jiayi; Xu, Yabin; Liu, Yuanpeng; Wang, Jun
• Is Part Of: Computer aided design. Volume 144(2022)
• Publication Date: 2022-03
• Subjects: Point cloud registration -- Multi-view registration -- Aircraft fuel tank inspection -- Sealant measurement; 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.2021.103164
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: The aircraft fuel tank, as the source of fuel supply, is one of the key components for an aircraft. It is fastened to the fuselage with thousands of rivets, in which each rivet is sealed carefully to prevent the leakage. Thus, the sealing quality of the aircraft fuel tank needs to be precisely measured to ensure its normal function. To achieve the measurement, the complete 3D point clouds captured by 3D scanning are usually utilized. Therefore, it is necessary to align the different views of the aircraft fuel tank point clouds, to obtain the complete data. However, the aircraft fuel tank consists of abundant repetitive structures, such as the regular-distributed rivets, which can lead to mismatches in the aircraft fuel tank point clouds if using the conventional registration methods. In order to address the challenges, we propose a new registration framework, which is able to handle the point cloud match under the repetitive and complex scenes. First, a novel 3D descriptor called LP-PPF is constructed with the point and line features from the aircraft fuel tank point clouds, so as to identify the repetitive structures correctly. Then, according to the proposed descriptor, the accurate registration can be achieved between the neighboring point cloud pairs. Finally, based on the results of pairwise registration, the complete aircraft fuel tank point cloud can be obtained via the multi-view registration method. Experiments demonstrate that our method achieves favorable results on both synthetic and raw scanned point cloud data. Highlights: An automatic registration framework for aircraft fuel tank point clouds. A novel 3D descriptor for accurate identification of repetitive structures. A feature filtering strategy for eliminating the ambiguity of 3D descriptors.