A point cloud splicing method for flat parts based on invariant features in multi-dimensional space

Abstract

The invention discloses a method for splicing point cloud of flat plate type parts based on multidimensional space invariant features. The nearest neighbor iterative algorithm is used to compute the rotation matrix of point cloud and multiple sampling radii. According to different sampling radii, the covariance matrices of each point at different scales are computed by using the features of pointcloud texture and point cloud density, and the covariance descriptors are constructed. A multiscale flow distance between two covariance descriptors is defined; The matching point pairs in the two point clouds are determined according to the distance of the flow pattern and the rough registration is performed to obtain the translation matrix. Then the nearest neighbor iterative algorithm is used to obtain the rotation matrix. The final transformation matrix is obtained and the splicing is completed. By defining covariance descriptors for each point in the point cloud, the invention realizes automatic splicing of the point cloud of a flat plate type part by utilizing density features and texture features with space invariant characteristics at a certain point, and is suitable for splicing of the point cloud of a plate type part with hole features.

Description

technical field [0001] The invention relates to the field of post-processing of three-dimensional point cloud data, and mainly relates to a method for splicing point clouds of flat-plate parts based on multi-dimensional space invariant features. Background technique [0002] In industrial production, flat-plate parts as cover parts, base plates or base parts are widely used. In order to ensure the production quality, it is necessary to test the forming quality of such flat parts. In recent years, as a non-contact point cloud acquisition method with high measurement accuracy, structured light 3D measurement technology has developed rapidly. More and more companies have begun to use this technology to reconstruct the structural dimensions of formed parts in 3D to Check whether the forming of parts meets the design requirements. Limited by the size of large flat parts, structured light 3D measurement equipment can often only measure from a single perspective. Since the point...

AI Technical Summary

Problems solved by technology

It can be seen that the artificial assisted method mainly has the following problems in the mosaic of multiple point clouds: First, the assisted mosaic method of artificially marked points has high requirements for the setting of the shape, size, and position of the marked points, and destroys the Integrity of the surface of the object; second, the positioning device is used to determine the position transformation matrix between multi-view point clouds. The stitching result is affected by the accuracy of the positioning device, and the equipment is large in size and poor in operation flexibility.

However, the above splicing method has the problem that the point cloud surface information used is less or the information is not easy to obtain, and the stability is poor, so it is difficult to splice large flat parts with few features

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