Article(id=1309639705931645613, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250184, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1746028800000, revisedDateStr=2025-05-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172037823, onlineDateStr=2026-09-23, pubDate=1771862400000, pubDateStr=2026-02-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172037823, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172037823, creator=13701087609, updateTime=1790172037823, updator=13701087609, issue=Issue{id=1309639699476606989, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='2', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1771862400000', pubDateStr='2026-02-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172036284, creator='13701087609', updateTime=1790232907749, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895012968661940, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895012968661941, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=48, endPage=55, ext={EN=ArticleExt(id=1309639706145555118, articleId=1309639705931645613, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Laser Point Cloud Denoising Algorithm Based on Ground Fitting and DBSCAN Clustering, columnId=1309639700797809024, journalTitle=Automobile Technology, columnName=Special Topic on LiDAR Perception and Multimodal 3D Object Detection Technology for Autonomous Driving, runingTitle=null, highlight=null, articleAbstract=

To address the challenges faced by conventional lidar systems in accurately filtering out water spray and dust, highly reflective expansion crosstalk noise, and the suboptimal removal of large-scale outliers, a laser point cloud denoising algorithm based on ground fitting and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed. Initial noise location information is obtained through ground point cloud segmentation, an enhanced DBSCAN algorithm, and a three-dimensional bounding box estimation method. Noise is then marked and filtered out based on its geometric and physical characteristics. The experimental results demonstrate that in dust and water spray scenarios, the noise-reduced Chamfer Distance (CD) decreases from 0.037 4 m to 0.019 8 m, while the Point-to-Mesh (P2M) distance reduces from 0.158 m to 0.088 m. The overall geometric deviation of the point cloud is significantly diminished, with improved conformity to the true surface. For processing complex point cloud data, the optimised DBSCAN clustering achieves a computational time of merely 6 ms compared to the conventional DBSCAN algorithm, representing a 77.8% improvement in computational efficiency. This effectively resolves the suboptimal outlier filtering performance of traditional laser point cloud denoising algorithms at large scales.

, authors=Jigang Wu1, Xingyu Zhou2, Peng Li2, authorsList=Jigang Wu, Xingyu Zhou, Peng Li, 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=1309639706447545007, articleId=1309639705931645613, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于地面拟合与DBSCAN聚类的激光点云去噪算法研究, columnId=1309639700932026754, journalTitle=汽车技术, columnName=激光雷达感知与自动驾驶多模态3D 目标检测技术专题, runingTitle=null, highlight=null, articleAbstract=

针对传统激光雷达难以精准滤除扬水扬尘、高反膨胀串扰噪点,且大尺度离群点滤除效果不佳等问题,提出一种基于地面拟合与基于密度的空间聚类(DBSCAN)的激光点云去噪算法。结合地面点云分割、改进的DBSCAN以及三维边界框估计方法获得噪点的初始位置信息,利用噪点的几何特征与物理特征标记并滤除噪点。试验结果表明:扬尘扬水场景中,去噪后的倒角距离(CD)从0.037 4 m降至0.019 8 m,点到网格(P2M)距离从0.158 m降至0.088 m,点云整体几何偏差显著减小且与真实表面拟合度提升;针对复杂点云数据的处理任务,相较于传统DBSCAN算法,优化后的DBSCAN聚类时间开销仅为6 ms,计算效率约提升77.8%,有效解决了传统激光雷达点云去噪算法在大尺度下离群点滤除效果不佳的问题。

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基于地面拟合与DBSCAN聚类的激光点云去噪算法研究
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吴纪刚 1 , 周星宇 2 , 李鹏 2
汽车技术 | 激光雷达感知与自动驾驶多模态3D 目标检测技术专题 2026,(2): 48-55
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汽车技术 |激光雷达感知与自动驾驶多模态3D 目标检测技术专题 2026 , (2) : 48 -55
基于地面拟合与DBSCAN聚类的激光点云去噪算法研究
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吴纪刚1, 周星宇2, 李鹏2
作者信息
  • 1.北京吉恩智车科技有限公司,北京 100080
  • 2.中国公路车辆机械有限公司,北京 100013
Laser Point Cloud Denoising Algorithm Based on Ground Fitting and DBSCAN Clustering
Jigang Wu1, Xingyu Zhou2, Peng Li2
Affiliations
  • 1.Beijing Gene Intelligent Car Technology Co., Ltd., Beijing 100080
  • 2.China Highway Vehicle & Machinery Co., Ltd., Beijing 100013
出版时间: 2026-02-24 doi: 10.19620/j.cnki.1000-3703.20250184
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针对传统激光雷达难以精准滤除扬水扬尘、高反膨胀串扰噪点,且大尺度离群点滤除效果不佳等问题,提出一种基于地面拟合与基于密度的空间聚类(DBSCAN)的激光点云去噪算法。结合地面点云分割、改进的DBSCAN以及三维边界框估计方法获得噪点的初始位置信息,利用噪点的几何特征与物理特征标记并滤除噪点。试验结果表明:扬尘扬水场景中,去噪后的倒角距离(CD)从0.037 4 m降至0.019 8 m,点到网格(P2M)距离从0.158 m降至0.088 m,点云整体几何偏差显著减小且与真实表面拟合度提升;针对复杂点云数据的处理任务,相较于传统DBSCAN算法,优化后的DBSCAN聚类时间开销仅为6 ms,计算效率约提升77.8%,有效解决了传统激光雷达点云去噪算法在大尺度下离群点滤除效果不佳的问题。

三维边界框估计  /  点云去噪  /  地面点云分割  /  DBSCAN聚类

To address the challenges faced by conventional lidar systems in accurately filtering out water spray and dust, highly reflective expansion crosstalk noise, and the suboptimal removal of large-scale outliers, a laser point cloud denoising algorithm based on ground fitting and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed. Initial noise location information is obtained through ground point cloud segmentation, an enhanced DBSCAN algorithm, and a three-dimensional bounding box estimation method. Noise is then marked and filtered out based on its geometric and physical characteristics. The experimental results demonstrate that in dust and water spray scenarios, the noise-reduced Chamfer Distance (CD) decreases from 0.037 4 m to 0.019 8 m, while the Point-to-Mesh (P2M) distance reduces from 0.158 m to 0.088 m. The overall geometric deviation of the point cloud is significantly diminished, with improved conformity to the true surface. For processing complex point cloud data, the optimised DBSCAN clustering achieves a computational time of merely 6 ms compared to the conventional DBSCAN algorithm, representing a 77.8% improvement in computational efficiency. This effectively resolves the suboptimal outlier filtering performance of traditional laser point cloud denoising algorithms at large scales.

3D bounding box estimation  /  Point cloud denoising  /  Ground point cloud segmentation  /  DBSCAN clustering
吴纪刚, 周星宇, 李鹏. 基于地面拟合与DBSCAN聚类的激光点云去噪算法研究. 汽车技术, 2026 , (2) : 48 -55 . DOI: 10.19620/j.cnki.1000-3703.20250184
Jigang Wu, Xingyu Zhou, Peng Li. Laser Point Cloud Denoising Algorithm Based on Ground Fitting and DBSCAN Clustering[J]. Automobile Technology, 2026 , (2) : 48 -55 . DOI: 10.19620/j.cnki.1000-3703.20250184

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doi: 10.19620/j.cnki.1000-3703.20250184
  • 首发时间:2026-09-23
  • 出版时间:2026-02-24
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    1.北京吉恩智车科技有限公司,北京 100080
    2.中国公路车辆机械有限公司,北京 100013
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