Research on dimension measurement based on 3D point cloud

In the manufacturing industry, the high-precision and high-efficiency dimension measurement of the large componentsisan important guarantee to improve product quality and production efficiency, but the traditional contact measurement method is low efficiency, poor accuracy, time consuming and vulnerable to human factors interference, has beenunableto meet the requirements of rapid and accurate measurement. To solve this problem, based on the three-dimensional point cloud data of large components, this paper studies the geometric feature extraction and dimension measurement methods of components. The 3D point clouds of components are preprocessed by establishing topological relationship, estimating surface normal vector and point clouds filtering for noise reduction. Geometric features of preprocessedpoint clouds are extracted, including point clouds with straight line features such as side edges and point clouds with circulararc features. The specific steps include extracting key planes by RANSAC, extracting edges of planes based onnormal vector estimation, retaining point clouds with geometric features, and dividing point clouds by Euclidean clustering. After that, the extracted point clouds with geometric features are synthesized into straight lines or circles to measurestraightness and roundness. Besides, a method is proposed to search adjacent points on the linear point clouds in order tomeasure arc length and analyze error sources and accuracy. The experimental results show that the measurement methodproposed in this paper can achieve high precision dimension measurement of the components.