An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data

Resumen

To further improve the efficiency and accuracy of the vehicle part inspection process, this paper designs an accurate and efficient vehicle body part inspection framework based on scattered point cloud data (PCD). Firstly, a hybrid filtering algorithm for point cloud denoising is designed to solve the problem of multiple noise points in the original point cloud measurement data. Secondly, a point cloud simplification algorithm based on Fuzzy C-Means (FCM) is designed to solve the problems of a large amount of data and many redundant points in the PCD. Thirdly, a point cloud fine registration algorithm based on the Teaching-Learning-based Optimization (TLBO) algorithm is designed to solve the problem where the initial point cloud measurement data cannot be located properly. Finally, the deviation distance between the PCD and Computer-Aided-Design (CAD) model is calculated by the K-Nearest Neighbor (KNN) algorithm to inspect and analyze the point cloud after preprocessing. On the basis of the design algorithm, four groups that contain measurement data for eight vehicle body parts are analyzed and the results prove the effectiveness of the algorithm, which is very suitable for the inspection process of vehicle body parts.