Locality preserving PCA-based three-dimensional point cloud registration method

Abstract

The invention discloses a locality preserving PCA-based three-dimensional point cloud registration method. In order to preserve local features of a point cloud, an LPP (Locality Preserving Projection)thought is adopted, and an adjacency graph and a complement graph of the point cloud are constructed through a K-neighbor criterion; adjacent points and non adjacent points are subjected to feature extraction in different processing modes; a conversion parameter is calculated through a feature matrix; and coordinate normalization is performed to finish registration. By adopting the technical scheme, a relatively good effect is achieved in registration of the point cloud with a remarkable local feature structure.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional reconstruction, and in particular relates to a three-dimensional point cloud registration method based on PCA. Background technique [0002] The 3D laser scanner adopts the laser ranging method, which can efficiently obtain the 3D data of the target. In order to obtain the complete 3D point cloud data of the object, it is necessary to convert the point cloud data from different perspectives into the same coordinate system through data registration. 3D point cloud registration is an important part of 3D reconstruction, and it has been widely used in reverse engineering, computer vision and other fields. Point cloud registration methods are divided into manual registration, instrument-dependent registration and automatic registration. At present, automatic registration is mainly divided into three types: registration based on feature finding correspondence, registration based on statisti...

AI Technical Summary

Problems solved by technology

However, the PCA point cloud registration algorithm only considers the overall structure during feature extraction, and cannot extract local structural features.

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