Position and Posture Measurements Using Laser Projection Markers for Infrastructure Inspection

Author:

Sasaki Tohru,Shioya Ryo,Sakai Toshitaka,Kinoshita Shunki,Nojiri Takahito,Terabayashi Kenji,Jindai Mitsuru

Abstract

Infrastructure such as roads, tunnels and bridges needs periodical inspection. The conventional structure inspection method, in which a human inspector uses a crack gauge, may lead to problems such as measurement errors and lengthy inspection times. Mobile robots and image processing have been used in the infrastructure inspection field. An image sensor or any sensor is used for measuring the state of the infrastructures. It is necessary that the mobile robot knows its own position and posture during automatic inspection. Generally, the Global Positioning System (GPS) has been used to sense the position of the mobile robot. However, GPS is not usable in places with the ceilings such as under bridges and in tunnels. Therefore, we developed a system to sense the position and posture of mobile robots. This system uses laser projection markers and cameras, and it has a very simple configuration. The camera photographs a target structure and projection laser markers, and the position and posture of the camera and mobile robot were calculated by an image application. The system makes infrastructure inspection more effective and decreases the time needed for inspection. In this paper, we examined the number of necessary laser markers, and we verified our method by experiment.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Alignment measurement of immersed tunnels based on laser differential imaging;Measurement;2024-07

2. CrackYOLO: Rural Pavement Distress Detection Model with Complex Scenarios;Electronics;2024-01-10

3. Automatic Location Algorithm for 3D Space Inspection Points in Substation Based on Deep Learning;2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC);2023-12-29

4. Cone–hole docking mechanism for a modular reconfigurable mobile robot and its characteristic analysis;Industrial Robot: the international journal of robotics research and application;2023-03-15

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