Article(id=1309640002900943663, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250704, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1763913600000, revisedDateStr=2025-11-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172108625, onlineDateStr=2026-09-23, pubDate=1787500800000, pubDateStr=2026-08-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172108625, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172108625, creator=13701087609, updateTime=1790172108625, updator=13701087609, issue=Issue{id=1309639998379491369, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1787500800000', pubDateStr='2026-08-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172107547, creator='13701087609', updateTime=1790232702427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894151819330406, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894151819330407, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=30, endPage=36, ext={EN=ArticleExt(id=1309640003077104432, articleId=1309640002900943663, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Optimization of Point Cloud Preprocessing for LiDAR-Inertial Ranging in Extreme Cold and Snowy Conditions, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the impact of blurred environmental features caused by snow and ice coverage on LiDAR point clouds during real-time laser-based construction and Simultaneous Localization and Mapping (SLAM) in extremely cold environments, this paper employs the LIO-SAM algorithm framework. In the point cloud preprocessing stage, a Dynamic Radius Outlier Removal (DROR) filter is used to eliminate snow-induced noise while preserving the details of environmental features, thereby providing high-quality point cloud data for subsequent processing by the LIO-SAM algorithm. Validation results on the CADC dataset show that, compared to the filter built into LIO-SAM, the point cloud data collected under snowy conditions achieves a recall rate exceeding 91% while maintaining high accuracy. This approach effectively removes snow-induced noise while preserving more environmental features.

, authors=Huaqi Zhao¹, Haoyu Wang¹, Jie Chen¹, Chongkang Xia¹, Guojing Li², authorsList=Huaqi Zhao, Haoyu Wang, Jie Chen, Chongkang Xia, Guojing Li, authorCompany=null, correspAuthors=Guojing Li, 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=1309640003261653809, articleId=1309640002900943663, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=极寒降雪环境中激光雷达-惯性测距的点云预处理优化, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对极寒降雪环境下激光雷达同步定位与建图（SLAM）中，雪花散射、积雪遮挡导致的环境特征模糊对激光雷达点云的影响，基于LIO-SAM算法框架，在点云预处理部分采用动态半径异常值去除（DROR）滤波器消除因雪引起的噪声，同时保留环境特征的细节，为LIO-SAM算法后续的点云处理提供高质量点云数据。在CADC数据集上的验证结果表明：相较于LIO-SAM自带的滤波器，试验在雪天条件下采集的点云数据，在保持较高精度的同时，召回率超过91%，有效去除雪点噪声并保留了更多的环境特征。

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