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Three-dimensional non-rigid point cloud registration method based on consistent point shift algorithm

A point cloud registration, non-rigid body technology, applied in computing, image data processing, instruments, etc., can solve the problems of low registration accuracy, long calculation time, noise, etc., and achieve the goal of improving registration accuracy and shortening running time Effect

Active Publication Date: 2017-09-05
哈尔滨工业大学人工智能研究院有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of low registration accuracy, poor robustness, Due to the shortcoming of long calculation time, a 3D non-rigid body point cloud registration method based on the consistent point drift algorithm is proposed

Method used

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  • Three-dimensional non-rigid point cloud registration method based on consistent point shift algorithm
  • Three-dimensional non-rigid point cloud registration method based on consistent point shift algorithm
  • Three-dimensional non-rigid point cloud registration method based on consistent point shift algorithm

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specific Embodiment approach 1

[0022] Specific implementation mode one: combine figure 1 To illustrate this embodiment, the specific process of the parallel processing method for 3D non-rigid body point cloud registration based on the consistent point drift algorithm in this embodiment is as follows:

[0023] Step 1. Use the image acquisition device in the painting robot to scan the object to be painted, and collect a set of three-dimensional point cloud data as the point cloud to be registered; figure 2 ;

[0024] Step 2. Preprocess the point cloud to be registered collected in step 1, and use the obtained point cloud data as a reference point set; image 3 ;

[0025] Step 3. Calculate the covariance σ between the reference point set obtained in step 2 and the existing template point set, and initialize the relevant parameters of the consistent point drift algorithm;

[0026] The relevant parameters of the consistent point drift algorithm include the coefficient matrix W, the weight ω that reflects the...

specific Embodiment approach 2

[0029] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the step 2, the point cloud to be registered obtained in the step 1 is preprocessed, and the obtained point cloud data is used as a reference point set; the specific process is :

[0030] Step 21, delete the background point cloud data that does not need to be registered in the point cloud to be registered collected in step 1, and obtain the point cloud after removing the background;

[0031] Step 22, using the statistical filter and the radius filter to delete the outlier points in the point cloud after removing the background obtained in step 21, to obtain the filtered point cloud;

[0032] Step two and three, down-sampling the filtered point cloud obtained in step two or two, to obtain the down-sampled point cloud; with sparse point cloud data, the purpose of reducing the amount of point cloud data is achieved;

[0033] Step 24: Save the down-sampled point cloud obtain...

specific Embodiment approach 3

[0035] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in Step 3, the covariance σ between the reference point set obtained in Step 2 and the existing template point set is calculated, and the consistent point drift is initialized The relevant parameters of the algorithm; the specific process is:

[0036] Step 31, the reference point set and the template point set are denoted as X N×D =(x 1 ,...x N ) T , Y M×D =(y 1 ,...y M ) T , then the covariance of the two point sets is initialized as:

[0037]

[0038] Among them, M and N are the number of points in the template point set and the reference point set respectively, and the value is a positive integer; D is the dimension of the point set; x n is the D-dimensional vector of the nth point in the reference point set, y m is the D-dimensional vector of the mth point in the template point set;

[0039] Step 32: Initialize the relevant parameters of the consistent point drift algorit...

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Abstract

The invention discloses a three-dimensional non-rigid point cloud registration method based on a consistent point shift algorithm, which relates to a spray painting robot position registration method based on point cloud registration and aims at solving defects of low registration precision, poor robustness and long calculation time existing in the present three-dimensional point cloud non-rigid registration algorithm in conditions of large point cloud data amount, complex deformation, noise, outliers and missing point influences. The method particularly comprises steps: 1, an image acquisition device in the spray painting robot is used for scanning an object waiting for spray painting, and a group of three-dimensional point cloud data is obtained as a to-be-registered point cloud; 2, the obtained point cloud data serve as a reference point set; 3, the covariance sigma between the reference point set and the existing template point set is calculated, and related parameters of the consistent point shift algorithm are initialized; 4, a Gauss kernel matrix is constructed; 5, the final registered result point set is obtained, and according to the final registered result point set, spray painting work is carried out on the object waiting for spray painting. The method of the invention is used for spray painting robot position registration.

Description

technical field [0001] The invention relates to a position registration method for a painting robot based on point cloud registration. Background technique [0002] With the requirements of modern industry on product quality and production efficiency, computer-aided manufacturing engineering has become an important part of modern computer science, and 3D point cloud registration technology is a more important link in computer-aided manufacturing engineering. In industrial production, point cloud registration can control industrial production measurement accuracy errors, quickly detect industrial product defects, and speed up industrial production progress. In addition, in non-industrial fields such as medical diagnosis and restoration of cultural relics, point cloud registration technology still plays a very important role, especially in the field of painting robots, the use of 3D point cloud registration technology can greatly improve the efficiency of robot painting. [0...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/35G06T7/32
CPCG06T7/32G06T7/35G06T2207/10028
Inventor 李湛滕军林伟阳高会军
Owner 哈尔滨工业大学人工智能研究院有限公司
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