IJAT Vol.11 No.4 pp. 657-665
doi: 10.20965/ijat.2017.p0657


Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution

Ryuji Miyazaki*,†, Makoto Yamamoto**, and Koichi Harada***

*Faculty of Psychological Science, Hiroshima International University
555-36 Gakuendai, Kurose, Higashi-hiroshima, Hiroshima 739-2695, Japan

Corresponding author

**Sanei Corporation, Hiroshima, Japan

***Graduate School of Engineering, Hiroshima University, Hiroshima, Japan

September 17, 2016
April 28, 2017
Online released:
June 29, 2017
July 5, 2017
MMS, point cloud, planar structure, boundary, region growing
We propose a line-based region growing method for extracting planar regions with precise boundaries from a point cloud with an anisotropic distribution. Planar structure extraction from point clouds is an important process in many applications, such as maintenance of infrastructure components including roads and curbstones, because most artificial structures consist of planar surfaces. A mobile mapping system (MMS) is able to obtain a large number of points while traveling at a standard speed. However, if a high-end laser scanning system is equipped, the point cloud has an anisotropic distribution. In traditional point-based methods, this causes problems when calculating geometric information using neighboring points. In the proposed method, the precise boundary of a planar structure is maintained by appropriately creating line segments from an input point cloud. Furthermore, a normal vector at a line segment is precisely estimated for the region growing process. An experiment using the point cloud from an MMS simulation indicates that the proposed method extracts planar regions accurately. Additionally, we apply the proposed method to several real point clouds and evaluate its effectiveness via visual inspection.
Cite this article as:
R. Miyazaki, M. Yamamoto, and K. Harada, “Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution,” Int. J. Automation Technol., Vol.11 No.4, pp. 657-665, 2017.
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