Point cloud registration method based on laser scanning

Abstract

The invention belongs to the technical field of three-dimensional reconstruction, and particularly relates to a point cloud registration method based on laser scanning. According to the method, the scanning point cloud data is simplified through voxel filtering, and the data operation amount is reduced. In a point cloud feature point extraction process, feature point extraction is carried out by using regional partitioning and normal difference features, so that the extracted feature points can well reserve geometric features of the point cloud and are uniformly distributed on a point cloud model. In the point cloud precise registration process, registration is performed by using the feature points, so that the point cloud search efficiency is improved, and the registration speed is accelerated. For the offset problem of the same position point of point cloud data obtained by laser scanning, registration is carried out by using a point-to-surface iterative nearest neighbor algorithm, and a point-to-surface matching error function is constructed through the minimum distance from a point cloud to a corresponding point tangent plane. And error point pairs are rejected through normal vector included angle constraint, so that the registration precision is improved, and accurate registration of the line laser scanning point cloud is realized.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional reconstruction, and in particular relates to a point cloud registration method based on laser scanning. Background technique [0002] Laser scanning systems are widely used in reverse engineering, 3D reconstruction and other fields to provide accurate 3D data for production practice. However, due to the limited scanning range of laser scanners, it is often necessary to scan objects from multiple angles, and convert point cloud data from different angles into the same coordinate system through point cloud data registration, so as to obtain a complete 3D outline model of the object. In the point cloud registration algorithm, the iterative closest point algorithm (ICP) is the most commonly used. This method traverses each point pair in the point cloud data to calculate the Euclidean distance. By iteratively obtaining the optimal transformation matrix, a good registration effect can be obtai...

AI Technical Summary

Problems solved by technology

However, this method does not consider the position offset of the data points obtained by each scan of the laser scanner, which makes the point cloud registration inaccurate and reduces the registration accuracy.

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