IJAT Vol.12 No.3 pp. 376-385
doi: 10.20965/ijat.2018.p0376


Curb Detection and Accessibility Evaluation from Low-Density Mobile Mapping Point Cloud Data

Kiichiro Ishikawa*,†, Daisuke Kubo**, and Yoshiharu Amano**

*Nippon Institute of Technology
4-1 Gakuendai, Miyashiro-machi, Saitama 345-8501, Japan

Corresponding author

**Waseda University, Tokyo, Japan

September 15, 2017
April 2, 2018
Online released:
May 1, 2018
May 5, 2018
mobile mapping system, curb accessibility, dynamic map, autonomous drive

Our goal is to automatically classify objects from Mobile Mapping System data to enable the automatic construction of dynamic maps. We aimed at the extraction of curbstones and classification of curb types. Although there is much research about curbstones being recognized from laser-scanned point clouds, there are few methods to classify curb types. In this paper, we propose a method to extract curbstones from low-density-type laser scan data. We also propose a method to distinguish whether curbstones allow access to off-road facilities. Evaluation tests give an F-measure of ≥94.4% and an accessibility classification accuracy of ≥99.6%. Moreover, the results of applying multiple filters to noise removal are compared.

Cite this article as:
K. Ishikawa, D. Kubo, and Y. Amano, “Curb Detection and Accessibility Evaluation from Low-Density Mobile Mapping Point Cloud Data,” Int. J. Automation Technol., Vol.12 No.3, pp. 376-385, 2018.
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