Measurement point cloud registration method for complex mechanical parts based on improved ICP

Abstract

Point cloud registration is the key step of 3D non-contact precision measurement for complex mechanical parts based on machine vision. Due to extreme dependence on initial position, slow iterative convergence and many wrong corresponding point pairs, Iterative Closest Point (ICP) algorithm could not satisfy the requirements of point cloud registration efficiency and precision for measurement of large quantities of complex mechanical parts, so an improved ICP method of measurement point cloud registration for complex mechanical parts by combining ICP with Intrinsic Shape Signature-Fast Point Feature Histogram (ISS-FPFH) feature is proposed. In order to reduce the registration quantity of the point cloud and keep the original subtle features on surface of point cloud, the voxel filter based on the point close to center of gravity is proposed to preprocess for point cloud down sampling. It is difficult for traditional ICP algorithm to determine the appropriate initial position and will lead to the registration failure of multi-view measurement point cloud, the Sample Consensus Initial Registration (SAC-IA) algorithm based on ISS-FPFH is applied for coarse registration. In order to solve the problems of slow iterative convergence and many wrong corresponding point pairs of traditional ICP algorithm, the point-to-plane ICP algorithm by combining with normal vector angle constraint is proposed for fine registration. Through experimental comparison and analysis, the results show that the accuracy and efficiency of the method proposed in this paper can meet requirements of 3D non-contact precision measurement for large quantities of complex mechanical parts.