Article(id=1309475203839783452, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475195581198782, articleNumber=null, orderNo=null, doi=10.19710/J.cnki.1003-8817.20250179, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790132817468, onlineDateStr=2026-09-23, pubDate=1776614400000, pubDateStr=2026-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790132817468, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790132817468, creator=13701087609, updateTime=1790132817468, updator=13701087609, issue=Issue{id=1309475195581198782, tenantId=1146029695717560320, journalId=1189873562199433220, year='2026', volume='', issue='4', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='1776614400000', pubDateStr='2026-04-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1790132815498, creator='13701087609', updateTime=1790133009559, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309476009590751261, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475195581198782, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309476009590751262, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475195581198782, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=17, endPage=25, ext={EN=ArticleExt(id=1309475204007555613, articleId=1309475203839783452, tenantId=1146029695717560320, journalId=1189873562199433220, language=EN, title=Point Cloud Alignment Method Based on Contour Cloud, columnId=null, journalTitle=Automobile Technology & Material, columnName=null, runingTitle=null, highlight=null, articleAbstract=
As a critical functional component of the vehicle body, the automobile engine hood's point cloud registration accuracy directly impacts the assembly gap, sealing performance, and collision safety. To address the limitations of traditional registration methods in handling engine hood point clouds—such as large surface registration errors, low precision, and poor efficiency—this paper employs an improved voxel filtering approach for preprocessing and proposes a hybrid registration method based on feature fusion and GICP. The method first performs coarse registration through curvature feature extraction and FPFH descriptors, followed by fine registration using the GICP algorithm. Experimental results demonstrate that our approach achieves an average registration error of 15.11 mm with an average processing time of 9.3 s on a dataset containing 30 engine hood point clouds, showing higher registration accuracy and better computational performance compared to baseline algorithms. This study provides a novel approach for high-precision registration of complex curved surfaces in automotive panels.
, authors=Tian Gu, Bo Yang, Jiahui Dong, Tao Jiang, Xiaojie Yang, Wenxiao Ren, authorsList=Tian Gu, Bo Yang, Jiahui Dong, Tao Jiang, Xiaojie Yang, Wenxiao Ren, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309475204305351198, articleId=1309475203839783452, tenantId=1146029695717560320, journalId=1189873562199433220, language=CN, title=汽车发动机罩点云配准方法研究, columnId=1190284220841955836, journalTitle=汽车工艺与材料, columnName=生产现场, runingTitle=null, highlight=null, articleAbstract=
汽车发动机罩的点云配准精度直接影响装配间隙、密封性能与碰撞安全性。针对传统配准方法在处理发动机罩点云时存在曲面配准误差大、配准精度低、效率差的问题,利用改进的体素滤波进行预处理,并提出一种基于特征融合与广义迭代最近点(GICP)的混合配准方法。该方法首先通过曲率特征提取与快速点特征直方图(FPFH)描述子实现粗配准,然后结合GICP算法实现精配准。以3种车型的发动机罩点云进行配准试验,在每种车型30组的数据集上平均配准误差为15.11 mm,平均耗时为9.3 s,较对照算法具有更高的配准精度和更好的运算性能,并且针对不完整、带有孔洞的点云配准效果依然较好,验证了算法具有较强的鲁棒性。
, authors=古田, 杨博, 董佳慧, 姜涛, 杨小杰, 任文晓, authorsList=古田, 杨博, 董佳慧, 姜涛, 杨小杰, 任文晓, authorCompany=null, correspAuthors=null, authorNote=
古田(1986—),男,工程师,硕士学位,研究方向为汽车尺寸工程数字化。
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古田(1986—),男,工程师,硕士学位,研究方向为汽车尺寸工程数字化。
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古田(1986—),男,工程师,硕士学位,研究方向为汽车尺寸工程数字化。
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