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Remote Sens. 2017, 9(5), 433; doi:10.3390/rs9050433

An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

1
School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Collaborative Innovation Centre of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jixian Zhang, Xiangguo Lin, Guoqing Zhou and Prasad S. Thenkabail
Received: 13 March 2017 / Revised: 9 April 2017 / Accepted: 30 April 2017 / Published: 3 May 2017
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
View Full-Text   |   Download PDF [4151 KB, uploaded 3 May 2017]   |  

Abstract

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. View Full-Text
Keywords: point cloud; plane segmentation; normal distribution transformation; RANSAC; NDT features point cloud; plane segmentation; normal distribution transformation; RANSAC; NDT features
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, L.; Yang, F.; Zhu, H.; Li, D.; Li, Y.; Tang, L. An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells. Remote Sens. 2017, 9, 433.

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