Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

To improve the accurate and sufficient recognition of abnormal points on the workpiece, a multidimensional anomaly point identification approach based on an improved eigenvalue method is proposed in this paper. Whether a point is normal or not depends on the angle between the two adjacent vectors wh...

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Main Authors: Guanglei Li, Yahui Cui, Lihua Wang, Lei Meng
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2020/4712916
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spelling doaj-b6801a8692c34f019b8dff330a63a5ba2020-11-25T02:49:21ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/47129164712916Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)Guanglei Li0Yahui Cui1Lihua Wang2Lei Meng3Xi’an University of Technology, Shaanxi, Xi’an 710048, ChinaXi’an University of Technology, Shaanxi, Xi’an 710048, ChinaXi’an University of Technology, Shaanxi, Xi’an 710048, ChinaXi’an University of Technology, Shaanxi, Xi’an 710048, ChinaTo improve the accurate and sufficient recognition of abnormal points on the workpiece, a multidimensional anomaly point identification approach based on an improved eigenvalue method is proposed in this paper. Whether a point is normal or not depends on the angle between the two adjacent vectors which consisted of four adjacent points around the current focus. The comprehensive judgment is carried out by multidimensional approximation. The numerical simulation and actual experiment validate the efficiency of the proposed method to quickly and accurately identify the abnormal point cloud in the surface point cloud data.http://dx.doi.org/10.1155/2020/4712916
collection DOAJ
language English
format Article
sources DOAJ
author Guanglei Li
Yahui Cui
Lihua Wang
Lei Meng
spellingShingle Guanglei Li
Yahui Cui
Lihua Wang
Lei Meng
Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)
Mathematical Problems in Engineering
author_facet Guanglei Li
Yahui Cui
Lihua Wang
Lei Meng
author_sort Guanglei Li
title Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)
title_short Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)
title_full Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)
title_fullStr Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)
title_full_unstemmed Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)
title_sort optimization of abnormal point cloud recognition in robot vision grinding system based on multidimensional improved eigenvalue method (miem)
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2020-01-01
description To improve the accurate and sufficient recognition of abnormal points on the workpiece, a multidimensional anomaly point identification approach based on an improved eigenvalue method is proposed in this paper. Whether a point is normal or not depends on the angle between the two adjacent vectors which consisted of four adjacent points around the current focus. The comprehensive judgment is carried out by multidimensional approximation. The numerical simulation and actual experiment validate the efficiency of the proposed method to quickly and accurately identify the abnormal point cloud in the surface point cloud data.
url http://dx.doi.org/10.1155/2020/4712916
work_keys_str_mv AT guangleili optimizationofabnormalpointcloudrecognitioninrobotvisiongrindingsystembasedonmultidimensionalimprovedeigenvaluemethodmiem
AT yahuicui optimizationofabnormalpointcloudrecognitioninrobotvisiongrindingsystembasedonmultidimensionalimprovedeigenvaluemethodmiem
AT lihuawang optimizationofabnormalpointcloudrecognitioninrobotvisiongrindingsystembasedonmultidimensionalimprovedeigenvaluemethodmiem
AT leimeng optimizationofabnormalpointcloudrecognitioninrobotvisiongrindingsystembasedonmultidimensionalimprovedeigenvaluemethodmiem
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