Point cloud data splicing method based on automatic identification of plurality of mark points

Abstract

The invention discloses a point cloud data splicing method based on automatic identification of a plurality of mark points. In an actual point cloud data splicing process, the corresponding positions of the mark points in the two blocks of the point cloud data cannot be entirely consistent due to the occurrence of a measurement error, so that an obtained conversion matrix causes an object to be converted to deform. For obtaining an optimal splicing effect, the automation of splicing point cloud can be realized from the view of actual application aiming to the property that the point cloud data is large and disordered for improving the sensitivity and the precision. On the premise of controlling the input mark points within a certain precision range at first, mark points can be used as a mark point group as many as possible. On the basis of said automatic searching and identifying algorithm, the mark points can be automatically aligned, so that a least square splicing algorithm of a characteristic group target function can be realized. A conversion matrix is calculated by a plurality of the points in a three-dimensional space, so that the calculation method is simple, and the splicing precision is high.

Description

technical field [0001] The invention belongs to the point cloud data splicing technology in the field of reverse engineering, and in particular relates to a point cloud data splicing method based on automatic recognition of multi-coordinate points. technical background [0002] What is researched and implemented is data preprocessing in reverse engineering, including point cloud processing, 3D space recognition, multi-view point cloud splicing, point cloud optimization processing, etc. This part of the processing determines the accuracy of 3D point cloud data with quality. The premise of establishing a CAD model is to have complete high-quality 3D point cloud data. The integrity and accuracy of 3D point cloud data largely determine the data quality that a CAD model can achieve. In the conventional 3D detection and conversion process, only a single point cloud data is obtained. The complete data model requires the splicing and processing of multiple point clouds, and its acc...

AI Technical Summary

Problems solved by technology

However, in practical applications, there is a lack of clear correspondence in many cases, so it is difficult to find the mapping relationship between each point of a point set, which makes the calculation speed unable to keep up, and it is very inconvenient for practical application.

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