多尺度点云噪声检测的密度分析法

doi:10.11947/j.AGCS.2015.20130423

测绘学报 ›› 2015, Vol. 44 ›› Issue (3): 282-291.doi: 10.11947/j.AGCS.2015.20130423

多尺度点云噪声检测的密度分析法

朱俊锋^1,2, 胡翔云², 张祖勋², 熊小东²

1. 中国测绘科学研究院, 北京 100830;
2. 武汉大学遥感信息工程学院, 湖北武汉 430079

收稿日期:2013-12-09 修回日期:2014-04-15 出版日期:2015-03-20 发布日期:2015-04-01
通讯作者: 胡翔云 E-mail:huxy@whu.edu.cn E-mail:huxy@whu.edu.cn
作者简介:朱俊锋(1986—),男,助理研究员,研究方向为数字摄影测量与计算机视觉. E-mail:junfeng_zhu@whu.edu.cn
基金资助:
国家自然科学基金(41271374);中国测绘科学研究基本科研业务专项资金(7771402);国家973计划(2012CB719904);国家863计划(2013AA063905);教育部博士研究生学术新人奖(5052012213001);中央高校基本科研业务费专项资金(2012213020207)

Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method

ZHU Junfeng^1,2, HU Xiangyun², ZHANG Zuxun², XIONG Xiaodong²

1. Chinses Academy of Surveying & Mapping, Beijing 100830, China;
2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received:2013-12-09 Revised:2014-04-15 Online:2015-03-20 Published:2015-04-01
Supported by:
The National Natural Science Foundation of China (No.41271374);Basic Scientific Research Projects of Chinese Academy of Surveying & Mapping (No.7771402);The National Basic Research Program of China(973 Program) (No.2012CB719904);The National High-tech Research and Development Program of China(863 Program) (No.2013AA063905);Academic Award for Excellent PhD Candidates Funded by Ministry of Education of China under Grant (No.5052012213001);The Fundamental Research Founds for the Central Universities (No.2012213020207)

摘要/Abstract

摘要：

当前机载激光雷达数据和影像匹配得到的点云是密集点云数据的两类主要来源,但都不可避免存在着噪声点.本文提出一种新的点云去噪算法,可适用于这两类数据中所包含的噪声点的去除.算法主要包括两步: 第1步利用多尺度的密度算法去除孤立噪声和小的簇状噪声;第2步利用三角网约束将第1步中误检测为噪声的点重新归为正常点.针对真实数据进行了剔噪试验,结果表明本文提出的基于密度分析的多尺度噪声检测算法对孤立噪声和簇状噪声都有较为效,且对于质量较差的影像匹配点云的检测也能有效处理.本文算法检测率达到97%以上.

关键词: 噪声检测, 点云数据, 多尺度, LiDAR, 影像匹配

Abstract:

Laser scanning and image matching are both effective ways to get dense point cloud data, however, outliers obtained from both ways are still inevitable. A novel hierarchical outlier detection method is proposed for the automatic outlier detection of point cloud from image matching and airborne laser scanning. There are two main steps in this method. Firstly, the hierarchical density estimation is used to remove single and small cluster outliers. Then a progressive TIN method is used to find non-outliers removed in the previous steps. The experimental results indicate the effectiveness of this method in dealing with the two types of points cloud data. And this method can also handle low quality point cloud data from image matching. The quantitative analysis shows that the outlier detection rate is higher than 97%.

Key words: outlier detection, points cloud data, hierarchical, LiDAR, image matching

中图分类号:

P237

朱俊锋, 胡翔云, 张祖勋, 熊小东. 多尺度点云噪声检测的密度分析法[J]. 测绘学报, 2015, 44(3): 282-291.

ZHU Junfeng, HU Xiangyun, ZHANG Zuxun, XIONG Xiaodong. Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(3): 282-291.

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多尺度点云噪声检测的密度分析法

Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method

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