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IJAT Vol.15 No.3 pp. 274-289
doi: 10.20965/ijat.2021.p0274
(2021)

Paper:

Research on Identification of Road Features from Point Cloud Data Using Deep Learning

Yoshimasa Umehara*1,†, Yoshinori Tsukada*2, Kenji Nakamura*3, Shigenori Tanaka*4, and Koki Nakahata*5

*1Organization for Research and Development of Innovative Science and Technology, Kansai University
3-3-35 Yamate-cho, Suita-shi, Osaka 564-0073, Japan

Corresponding author

*2Faculty of Business Administration, Setsunan University, Neyagawa, Japan

*3Faculty of Information Technology and Social Sciences, Osaka University of Economics, Osaka, Japan

*4Faculty of Informatics, Kansai University, Takatsuki, Japan

*5Graduate School of Informatics, Kansai University, Takatsuki, Japan

Received:
October 30, 2020
Accepted:
February 10, 2021
Published:
May 5, 2021
Keywords:
i-Construction, road feature, point cloud data, deep learning, feature identification
Abstract

Laser measurement technology has progressed significantly in recent years, and diverse methods have been developed to measure three-dimensional (3D) objects within environmental spaces in the form of point cloud data. Although such point cloud data are expected to be used in a variety of applications, such data do not possess information on the specific features represented by the points, making it necessary to manually select the target features. Therefore, the identification of road features is essential for the efficient management of point cloud data. As a technology for identifying features from the point cloud data of road spaces, in this research, we propose a method for automatically dividing point cloud data into units of features and identifying features from projected images with added depth information. We experimentally verified that the proposed method accurately identifies and extracts such features.

Cite this article as:
Yoshimasa Umehara, Yoshinori Tsukada, Kenji Nakamura, Shigenori Tanaka, and Koki Nakahata, “Research on Identification of Road Features from Point Cloud Data Using Deep Learning,” Int. J. Automation Technol., Vol.15, No.3, pp. 274-289, 2021.
Data files:
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Last updated on Jul. 20, 2021