Article(id=1321893320473821991, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751558400000, receivedDateStr=2025-07-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093527191, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093527191, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093527191, creator=13701087609, updateTime=1793093527191, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=218, endPage=224, ext={EN=ArticleExt(id=1321893320658371368, articleId=1321893320473821991, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on an Adaptive Progressive Point Cloud Filtering Algorithm for Deformation Monitoring of Mine Roadways, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Point cloud data of mine roadways acquired by three-dimensional laser scanning often contain noise interference. Existing filtering methods struggle to balance point cloud data simplification with the accurate preservation of roadway geometric features. In contrast, adaptive filtering can dynamically adjust filtering parameters based on the characteristics of the original data, thereby providing a high-quality data foundation for subsequent roadway deformation monitoring. An adaptive progressive point cloud filtering algorithm was proposed, which integrated an improved adaptive voxel filter and a layer-constrained dynamic radius filter to achieve precise point cloud denoising. Specifically, the improved voxel filter can adaptively adjust voxel grid sizes according to the geometric complexity of the roadway, effectively simplifying the point cloud while better preserving its geometric features. The layered constraint dynamic radius filter optimized the dynamic radius parameters through a layered constraint model, enabling the accurate elimination of residual noise. Experimental results show that, compared with traditional voxel filtering, the improved voxel filter, and their conventional combinations, the proposed algorithm achieves a point cloud simplification rate exceeding 92% while controlling the change rates of point cloud volume and surface area within 3%. It can provide reliable data preprocessing technical support for mine roadway deformation monitoring.

, authors=Lei Wang¹, Jingjing Zhang², Junyan Qi², Zhuli Ren³, authorsList=Lei Wang, Jingjing Zhang, Junyan Qi, Zhuli Ren, authorCompany=null, correspAuthors=Jingjing Zhang, 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=1321893320884863785, articleId=1321893320473821991, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=面向矿井巷道变形监测的自适应递进点云滤波算法研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

三维激光扫描获取的矿井巷道点云数据往往伴有噪声干扰，现有滤波方法难以兼顾点云数据精简与巷道几何特征精准保留，而自适应滤波可根据原始数据特征动态调整滤波参数，能够为后续巷道变形监测提供高质量数据基础。提出一种自适应递进式点云滤波算法，融合改进自适应体素滤波和分层约束动态半径滤波实现点云精准去噪。其中，改进体素滤波可根据巷道几何复杂程度自适应调节体素网格尺寸，在高效精简点云的同时更好保留巷道几何特征；分层约束动态半径滤波通过分层约束模型优化动态半径参数，实现残余噪声的精准剔除。试验结果表明，与传统体素滤波、改进体素滤波及其常规组合方法相比，所提算法的点云简化率超92%，同时将点云体积变化率、表面积变化率控制在3%以内，可为矿井巷道变形监测提供可靠的数据预处理技术支撑。

, authors=王磊¹, 张晶晶², 齐俊艳², 任助理³, authorsList=王磊, 张晶晶, 齐俊艳, 任助理, authorCompany=null, correspAuthors=张晶晶, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=nyp6RLvbANKbqWi9S1KD0Q==, magXml=TXzK7eoA/Pct74oNd6ZrRw==, pdfUrl=null, pdf=/BnoTyLW+dWppW7VW45kCw==, pdfFileSize=3950582, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=xe0SXk+YHk+Yg+M8oPm0rQ==, mapNumber=null, fund=null)}, authors=[Author(id=1321893322990404404, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893320473821991, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wlqjy@hpu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893323078484790, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893320473821991, authorId=1321893322990404404, language=EN, stringName=Lei Wang, firstName=Lei, middleName=null, lastName=Wang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454003, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893323149787959, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893320473821991, authorId=1321893322990404404, language=CN, stringName=王磊, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．河南理工大学 计算机科学与技术学院，河南 焦作市　454003, bio={"content":"

王磊（1977—），男，河南焦作人，博士，教授，主要从事矿山安全方面的研究工作。E-mail:wlqjy@hpu.edu.cn

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王磊（1977—），男，河南焦作人，博士，教授，主要从事矿山安全方面的研究工作。E-mail:wlqjy@hpu.edu.cn

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