Point cloud registration method with a low overlapping rate

Abstract

The invention discloses a point cloud registration method with a low overlapping rate. The point cloud registration method comprises the following steps: step 1, obtaining a source point cloud and a target point cloud; step 2, performing downsampling by using the obtained source point cloud and target point cloud to obtain a point cloud A and a point cloud B respectively; step 3, respectively segmenting the point cloud A and the point cloud B into a plurality of local areas; step 4, performing preliminary matching, and selecting a corresponding four-point congruent set is through a 4PCS algorithm; step 5, selecting the optimal transformation of the local area; step 6, constructing a matching confidence coefficient of the local area, enumerating and calculating the matching condition of allthe local areas, sorting the local areas, and selecting the local area with the highest confidence coefficient; step 7, performing registration by using an LMidS sampling consistency algorithm to obtain a final rigid transformation matrix, and completing point cloud registration; According to the invention, good registration precision can be ensured under the condition of low overlapping rate.

Description

technical field [0001] The invention relates to the technical field of three-dimensional reconstruction, in particular to a point cloud registration method with low overlap rate. Background technique [0002] In recent years, with the increasing maturity of sensor technology and the improvement of computer computing performance, 3D scanning hardware equipment has continued to develop. The emergence of the Kinect sensor has enabled researchers to obtain cheaper 3D point cloud data; The scanning angle is limited, and each scan can only measure a part of the point cloud data of the object; but in practical applications, the complete point cloud data of the measured object is usually required; this requires scanning the 3D points of the object from different perspectives through point cloud registration The cloud data is transformed into the same coordinate system; the transformation relationship between the point clouds of different perspectives is estimated according to the po...

AI Technical Summary

Problems solved by technology

[0003]Point cloud registration is an important part of 3D reconstruction, and it is widely used in reverse engineering, computer vision, robot real-time positioning and mapping, 3D scanning, digital archaeology and other fields. application; the classic ICP algorithm is currently the most widely used point cloud registration algorithm, but the premise of the traditional ICP algorithm requires that the point cloud overlap rate is greater than 50%, and a good initial value must be set for it so that it will not fall into Local optimal solution does not guarantee convergence to the global optimal result

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