Vehicle Self-Position Estimation Using Lighting Recognition in Expressway Tunnel for Visual Inspection Flow-Reference-Cited by-同舟云学术

Vehicle Self-Position Estimation Using Lighting Recognition in Expressway Tunnel for Visual Inspection Flow

Published:2024-06-20 Issue:3 Volume:36 Page:746-757
ISSN:1883-8049
Container-title:Journal of Robotics and Mechatronics
language:en
Short-container-title:JRM

Author:

Moko Yushi¹^ORCID,Hiruma Yuka²,Hayakawa Tomohiko¹²^ORCID,Ke Yushan¹,Onishi Yoshimasa³,Ishikawa Masatoshi¹^ORCID

Affiliation:

1. Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 113-8601, Japan

2. Information Technology Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

3. Central Nippon Expressway Company Limited, 2-18-19 Nishiki, Naka-ku, Nagoya, Aichi 460-0003, Japan

Abstract

In this study, a stable and high-speed vision-based self-position estimation method was proposed that improves upon the existing method of lane detection by recognizing the lighting facilities that are installed in tunnels on Japanese expressways where GNSS cannot be used. In addition, we proposed a method for inspecting multiple cracks at once by estimating the self-position with the successful rate 75% in the traveling direction by counting the lighting with the successful rate 99.85%. The effectiveness of the method was verified by capturing images of cracks in an actual tunnel. The proposed method will enable more frequent inspections for tunnel cracks that lead to flaking while maintaining infrastructure safety, reducing costs, and improving tunnel visual inspection flow efficiency.

Publisher

Fuji Technology Press Ltd.

Reference25 articles.

1. J. A. Richards, “Inspection, maintenance and repair of tunnels: International lessons and practice,” Tunnelling and Underground Space Technology, Vol.13, No.4, pp. 369-375, 1998. https://doi.org/10.1016/S0886-7798(98)00079-0

2. T. Yasuda, H. Yamamoto, and Y. Shigeta, “Tunnel Inspection System by using High-speed Mobile 3D Survey Vehicle: MIMM-R,” J. of the Robotics Society of Japan, Vol.34, No.9, pp. 589-590, 2016 (in Japanese). https://doi.org/10.7210/jrsj.34.589

3. S. Nakamura, A. Yamashita, F. Inoue, D. Inoue, Y. Takahashi, N. Kamimura, and T. Ueno, “Inspection Test of a Tunnel with an Inspection Vehicle for Tunnel Lining Concrete,” J. Robot. Mechatron., Vol.31, No.6, pp. 762-771, 2019. https://doi.org/10.20965/jrm.2019.p0762

4. K. Suzuki, H. Yamaguchi, K. Yamamoto, and N. Okamoto, “Tunnel inspection support service using 8K area sensor camera,” J. of the Japan Society of Photogrammetry, Vol.60, No.2, pp. 40-41, 2021 (in Japanese). https://doi.org/10.4287/jsprs.60.40

5. T. Hayakawa, Y. Moko, K. Morishita, and M. Ishikawa, “Pixel-wise deblurring imaging system based on active vision for structural health monitoring at a speed of 100 km/h,” Prof. of SPIE, Vol.10696, 10th Int. Conf. on Machine Vision (ICMV 2017), pp. 548-554, 2018. https://doi.org/10.1117/12.2309522