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A high-efficiency method of pantograph collector strip wearing inspection based on stereo vision

机译:基于立体视觉的集电弓集电带磨损检测的高效方法

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Pantograph is a power collector to get current from the overhead catenary for electric train engine. When the train moves forward, the high frequency friction between pantograph and catenary gives rise to the wear of the carbon strips. In this paper, we propose an efficient stereo-based method to non-contact monitor the pantograph wearing. Firstly, a modified SGM-based stereo matching algorithm is raised to overcome the complicated illumination in the actual environment. Secondly, the point cloud segmentation using Radius Filter and Density-based Spatial Clustering of Applications with Noise (RF-DBSCAN) is carried out to eliminate noisy points caused by mismatching and extract the carbon strips from background. Finally, for the purpose of wearing detection, the processed point cloud is aligned with stand CAD model by the proposed coarse-to-fine iterative closest point (CF-ICP) method. The experimental data which is obtained from a real train maintenance depot reveals that root mean square (RMS) of the wearing inspection is less than 4 mm, which outperform than other classic methods
机译:受电弓是一种集电器,可从电动火车发动机的架空链上获取电流。当火车向前行驶时,受电弓和悬链线之间的高频摩擦会引起碳带的磨损。在本文中,我们提出了一种有效的基于立体声的方法来非接触式监测受电弓的磨损。首先,提出了一种改进的基于SGM的立体匹配算法,以克服实际环境中的复杂照明。其次,使用半径过滤器和基于密度的带有噪声的应用程序空间聚类(RF-DBSCAN)进行点云分割,以消除由不匹配导致的噪点,并从背景中提取碳条。最后,出于磨损检测的目的,通过提出的从粗到细的迭代最近点(CF-ICP)方法,将处理后的点云与支架CAD模型对齐。从实际的火车维修站获得的实验数据表明,磨损检查的均方根(RMS)小于4 mm,这比其他经典方法要好

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