# An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data
## 1 Introduction  
In the field of vehicle engineering, the design, manufacturing, inspection and evaluation of body parts constitute the whole life cycle of vehicle part production under the background of \"Industry 4.0\" [1]. Each of the above links will have a great impact on the final matching process of the vehicle body. In the production of the parts, the inspection and evaluation processes of the parts, in later stages of production, is the key to ensuring the accuracy of the parts and the quality of late matching. According to the actual engineering experience, rework or repair caused by the incorrect matching of parts accounts for a relatively high proportion [2]. On the other hand, given the continuous growth in the market demand for automobile quality, the matching quality requirements of the parts that have the greatest influence on body shape are correspondingly increasing. Generally speaking, the inspection process of the parts at this stage mainly includes two aspects--one is the manufacturing status of parts themselves, such as the evaluation of dimensional tolerance, form and position error; the other is the matching state of parts with other parts--that is, gap and interference evaluations between the body parts. However, no matter the necessary inspection and evaluation processes, the technical parameters of the parts themselves should be satisfied first. Above all, as a key link of each product's lifecycle, the quality inspection of mechanical parts has been given increasing attention. In the process of vehicle assembly, during the actual late matching process, some parts of the body will be deformed during stamping, welding and painting prior to final assembly, which will directly affect the assembly accuracy of the whole vehicle [3]. Therefore, to ensure the quality of the late assembly of the parts, engineers and technicians estimate the matching state of the parts by means of measurements and later data processes to predict and adjust the parts in the later stage. However, traditional manual inspection techniques have low matching accuracy, a tedious matching process and a large workload. To further improve the matching precision of parts and guarantee the quality of the body, a new technical scheme based on scattered point cloud data (PCD) has been increasingly used in the assembly process [3,4]. This new inspection process relies on high data quality. Poor data quality which contain many invalid and redundant points can lead to many problems, such as high computational demand of point cloud processing, low work efficiency and large errors in the matching results. Therefore, the inspection process of vehicle body parts has been a research subject with practical engineering value. The development of computer technology and three-dimensional (3D) point cloud measurement technology has provided a reliable technical tool for vehicle parts inspection. As part of an interdisciplinary field, the basic principle of this technology is to obtain PCD coordinate information for manufacturing molded parts by scanning them on their surface and to preprocessing them through denoising, simplification, registration and so forth, to provide reliable PCD data for subsequent quality inspection analyses. However, for the pre-processing step of PCD, selecting or designing effective algorithms can improve the inspection accuracy of vehicle body parts. Therefore, further design and research of related algorithms are needed. On the basis of the above factors, this paper aims to study a set of computing processes to improve the inspection efficiency and accuracy of the vehicle body parts based on scattered PCD. We used the actual project data to analyze the location relationship between the scattered PCD to provide important data support and technical guarantee for the inspection of vehicle body quality. The main contributions of this paper are as follows:  
1.