A Method for Automatically Analyzing 3D Point Cloud Registration Errors Based on Stereo Grid

Abstract

The invention discloses a method of automatically analyzing a 3D point cloud registering error, based on stereo grids. The method of automatically analyzing a 3D point cloud registering error, based on stereo grids includes the following steps: 1) obtaining a source point cloud; 2) obtaining a transformation matrix; 3) performing down-sampling on the source point cloud; 4) filtering the source point cloud; 5) obtaining the target point cloud; 6) extracting the characteristic points; 7) calculating the characteristic vector of the characteristic points; 8) looking for the corresponding points between the source point cloud and the target point cloud; 9) calculating the average distance between the corresponding points; 10) when the number of the discovered corresponding points is reduced to be less than the threshold, entering the step 11, or modifying the control variable looking for the corresponding points, and returning to the step 8; and 11) through weighted average, obtaining the error of the laser radar 3D point cloud registering. The method of automatically analyzing a 3D point cloud registering error, based on stereo grids can quantitatively compare the accuracy of using different registering algorithms to construct a 3D scene model, can help the registering algorithm to determine the suitable point cloud scale in practical application, and can be used to the laser radar three dimensional plotting and structural measurement field with higher requirement for registering accuracy.

Description

technical field [0001] The invention relates to a data processing method for 3D point clouds, in particular to a method for automatically analyzing registration errors of 3D point clouds based on a three-dimensional grid, which can be used in laser radar three-dimensional mapping and structural measurement that require high registration accuracy field. Background technique [0002] 3D laser scanning technology can quickly and efficiently obtain the 3D coordinates of the surface of the measured object, and is currently widely used in surveying and mapping engineering, structural measurement, cultural relics protection and other fields. However, due to the limitation of the scanning range of the 3D lidar, it is sometimes necessary to scan at multiple stations and from multiple angles, or to scan simultaneously with multiple lidars. Therefore, it is necessary to use 3D point cloud data fusion and registration technology to register multiple point clouds in the same coordinate ...

AI Technical Summary

Problems solved by technology

[0007] The second is the impact on the registration accuracy. If the number of points is small, some points containing key information and features may be filtered out, resulting in an increase in the number of wrong corresponding point pairs during registration.

[0008] Most of the current methods for calculating the error of registration algorithms are only calculated on the basis of their own registration methods, so they are not universal and comparable

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