Point cloud registration method for point neighborhood scale difference description

Abstract

The invention provides a point cloud registration method for point neighborhood scale difference description. The method comprises the following steps: performing least square surface fitting on discrete points in a source point cloud and a target point cloud to obtain a local surface, solving a shape index SI of the surface, i.e., the shape index of the discrete points, and selecting a point of which the shape index is maximum or minimum in a neighborhood and meets a threshold value as a key point of the point cloud; carrying out feature descriptor construction, and calculating feature normalization vector difference values and normal vector included angle difference values of the key points under different neighborhood radiuses to be combined into a point domain scale difference descriptor; finding out corresponding points according to the similarity degree of the feature descriptors, and filtering out error point pairs and estimating the corresponding relation through double screening and an optimal searching algorithm based on the global distance. The key points obtained by the method have good representativeness and distinctiveness, the effect on the condition that the point cloud distribution density difference is large or noise points exist is obvious, the calculation is simple, the point cloud registration speed and precision are improved, and the method has good anti-interference capability.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a point cloud registration method for point neighborhood scale difference description. Background technique [0002] With the development of many new sensing technologies, the acquisition of point cloud data is becoming more and more efficient. 3D point cloud data processing technology has also developed rapidly, and has been widely used in many fields such as computer graphics, CAD modeling design, cultural relics and historic sites protection, automobile manufacturing and mold manufacturing. However, due to factors such as the external environment, the size of the object, and the limitations of the scanning device itself, the complete surface shape of the object cannot be obtained from the point cloud data of a single scan. Therefore, efficient point cloud registration algorithms are needed to stitch point cloud data acquired from different perspectives. Among the m...

AI Technical Summary

Problems solved by technology

The FPFH feature needs to calculate the angle features of any point and its k-nearest neighbors connected to each other in pairs, the calculation amount is relatively large, and the calculation efficiency is low; the description of the feature information of the LBP operator is too simple; the calculation of the SHOT operator is complicated due to its high latitude

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