Visual Localization of an Internal Inspection Robot for the Oil-Immersed Transformer

IF 1.4 Q4 ROBOTICS Journal of Robotics Pub Date : 2023-12-29 DOI:10.1155/2023/6699265
Yingbin Feng, Yahui Kou, Yanju Liu
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Abstract

Aiming at the problem that the robot is difficult to locate in the oil-immersed transformer, a visual positioning of the robot is proposed for internal inspection. First, in order to solve the problem of blur, distortion, and low contrast of the image obtained by the camera in the deteriorated and discolored transformer oil, an image enhancement algorithm based on multiscale fusion is developed to provide a reliable data source for robot localization. Then, the FAST key points are extracted and the BRIEF descriptors are calculated from the enhanced images, and the pose transformation of the robot between image frames is calculated by using polar constraint and EPnP method. A pose optimization model of the robot is designed to improve the positioning accuracy. Finally, to verify the effectiveness of the proposed methods, function tests are carried out by using the real continuous image sequence acquired by the robot in Mitsubishi transformer. The experimental results show that the trajectory of the robot in the transformer can be accurately drawn, the position data of the robot can be efficiently obtained, and autonomous positioning of the robot in the transformer can be well achieved.
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油浸式变压器内部检测机器人的可视化定位系统
针对机器人在油浸式变压器中难以定位的问题,提出了一种机器人视觉定位的内部检测方法。首先,为了解决变质变色变压器油中摄像头获取的图像模糊、失真和对比度低的问题,开发了一种基于多尺度融合的图像增强算法,为机器人定位提供可靠的数据源。然后,从增强后的图像中提取 FAST 关键点并计算 BRIEF 描述符,利用极性约束和 EPnP 方法计算机器人在图像帧间的姿态变换。设计了机器人姿态优化模型,以提高定位精度。最后,为了验证所提方法的有效性,使用机器人在三菱变压器中获取的真实连续图像序列进行了功能测试。实验结果表明,机器人在变压器中的运动轨迹可以精确绘制,机器人的位置数据可以有效获取,机器人在变压器中的自主定位可以很好地实现。
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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