太阳成8722(中国)有限公司-GREEN NO.1

标题：An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells作者：Li, L (Li, Lin); Yang, F (Yang, Fan); Zhu, HH (Zhu, Haihong); Li, DL (Li, Dalin); Li, Y (Li, You); Tang, L (Tang, Lei)

来源出版物：REMOTE SENSING 卷：9 期：5文献编号：433 DOI：10.3390/rs9050433 出版年：MAY 2017

摘要：Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

入藏号：WOS:000402573700036

文献类型：Article

语种：English

作者关键词： point cloud; plane segmentation; normal distribution transformation; RANSAC; NDT features

扩展关键词： LASER-SCANNING DATA; BUILDING ROOFS; RECONSTRUCTION; REGISTRATION

通讯作者地址： Li, L (reprint author), Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

Li, L (reprint author), Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

电子邮件地址： lilin@whu.edu.cn; yhlx125@163.com; hhzhu@whu.edu.cn; lidalin@whu.edu.cn; boycecug@gmail.com; leitang@whu.edu.cn

地址：

[Li, Lin; Yang, Fan; Zhu, Haihong; Li, Dalin; Li, You; Tang, Lei] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Li, Lin] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

研究方向：Remote Sensing

ISSN：2072-4292

影响因子：3.244