A parallel three-dimensional point cloud data automatic registration method

A 3D point cloud and data technology, applied in image data processing, instruments, calculations, etc., can solve the problems of affecting the registration effect, huge time cost, and high accuracy requirements of artificial standards, so as to reduce computational complexity and improve accuracy , to avoid the effect of misregistration

Active Publication Date: 2019-04-02
NORTHEASTERN UNIV
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Problems solved by technology

Manual registration needs to manually mark multiple corresponding points in the two point cloud data in advance, and then solve the homogeneous equation of rigid transformation according to the marked corresponding points to solve the rotation matrix R and translation vector T. Finally, according to the obtained R and T, the The coordinate systems of the two point clouds are unified. This method has extremely high requirements on the accuracy of the artificial standard, and the wrong standard

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  • A parallel three-dimensional point cloud data automatic registration method
  • A parallel three-dimensional point cloud data automatic registration method
  • A parallel three-dimensional point cloud data automatic registration method

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Embodiment Construction

[0036] The present invention provides a parallel automatic registration method for 3D point cloud data, such as Figure 4 shown, including the following steps:

[0037] Step 1: Obtain the source point cloud P and target point cloud Q to be registered under different views;

[0038] Step 2: Point cloud downsampling processing. The downsampling process is performed on large-scale 3D point cloud data to reduce the amount of calculation while retaining the characteristics of the point cloud data to the greatest extent. Here, the scale-invariant feature transformation 3D-SIFT is used to extract the key points in the point cloud as the result of downsampling. In particular, for the field of 3D reconstruction, the sparse point cloud can be directly used as the downsampling result. The number of key points N in the source point cloud P after downsampling p , the number of key points M in the target point cloud Q q .

[0039] Step 3: Calculate the normal vector for the points in ...

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Abstract

The invention discloses a parallel three-dimensional point cloud data automatic registration method. The method comprises the steps of obtaining two to-be-registered point clouds which are in different views and have overlapped regions; downsampling processing is carried out on the point cloud to reduce the calculation amount; calculating a normal vector of the point cloud data and calculating a fast point feature histogram FPFH feature; starting a plurality of processes to select n points from one point cloud and search corresponding points from the other point cloud, calculating a rotation translation matrix of rigid transformation according to the corresponding points, calculating an error measurement standard, taking a result of the minimum error measurement standard as an iteration result of this time, carrying out iteration for multiple times, and taking a final result as a transformation matrix; and finally, performing fine registration by using an ICP iteration algorithm.

Description

technical field [0001] The invention belongs to the field of point cloud data processing, in particular to a parallel automatic registration method for three-dimensional point cloud data. Background technique [0002] 3D point cloud data is a collection of point data on the appearance surface of objects obtained by measuring instruments, and is a digital representation of the real world. 3D point cloud data has strong application value in building protection, 3D maps, biomedicine and other fields. 3D point cloud data will encounter the problem of unifying the point cloud data acquired by two different views into the same coordinate system, that is, point cloud registration. [0003] The current point cloud registration technology is mainly divided into manual registration and automatic registration. Manual registration needs to manually mark multiple corresponding points in the two point cloud data in advance, and then solve the homogeneous equation of rigid transformation...

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Application Information

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IPC IPC(8): G06T7/33
CPCG06T7/33G06T2207/10028
Inventor 杨晓春王斌冯策
Owner NORTHEASTERN UNIV
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