Establishing a Localization Method for Mobile Robots to Support Bridge Inspection

Song, Hyunwoo; Nakahama, Jun; Takada, Yogo

doi:10.1299/jsmesed.2019.18.213

Cited by 1 publication

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“…However, it is difficult to detect the position of the robot as coordinates using the Global Navigation Satellite System (GNSS) because data from satellites cannot be received inside large buildings, such as inside bridge box girders. Therefore, using the characteristics of the light detection and ranging (LiDAR) sensor, we developed the planar LiDAR, which detects the position of the robot as coordinates based on the distance and direction data from the feature points fixed in the experimental environment, and installed it in BIREM-IV [19]. Figure 1, Figure 2, and Table 1 present the appearances, components, and specifications of BIREM-IV-P, respectively.…”

Section: Introductionmentioning

confidence: 99%

Localization Method Based on Image Processing for Autonomous Driving of Mobile Robot in the Linear Infrastructure

Song¹,

Nakahama²,

Takada³

2021

ACIS

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In recent years, the deterioration of infrastructure facilities such as bridges has become a problem. Precautionary measures such as visual inspection and repair by humans are in place as countermeasures for aging; however, there are issues with cost and safety in such inspections. If inspection by robots becomes possible, both these aspects will be improved, which will significantly contribute to the maintenance of infrastructure facilities. In this paper, we propose a complex image processing technique to specify the location of feature points as coordinates through smartphone cameras to obtain the location information of feature points needed for positioning BIREM-IV-P developed to support bridge inspection. The corners located in the bridge inspection environment are used as feature points, and the corners are specified using Harris corner detection, which is a conventional corner detection method, to obtain the position of the feature points. In addition, to compensate for the shortcomings of Harris corner detection, a line segment in the image is detected using the Hough transform, and the intersection points of the line segments are recognized as corners. By combining the results of the two detection methods in this manner, the target feature points can be accurately specified. Then, the position of the feature points of the specified image coordinate system can be changed to the world coordinate system. As a result, it was possible to detect the location of the target feature points in a three-dimensional coordinate system.

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Section: Introductionmentioning

confidence: 99%