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Pipe radius estimation using Kinect range cameras

机译:使用Kinect测距相机估算管道半径

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In the heavy construction industry, pipe spool assembly is geometrically complex and suboptimal fabrication processes inevitably lead to fabrication errors and costly rework. In an attempt to mitigate fabrication risks and improve product quality, computer aided tools are being developed to provide an additional layer of control. In this paper, the data acquisition capabilities of two low-cost range cameras are investigated for the eventual purpose of pipe fitting process monitoring in a smart fabrication facility environment. Range images of various pipes are systematically taken at varying distances from the sensor. By using the proposed radius estimation algorithm, the utility of the data for accurate geometrical pipe feature detection is evaluated by a radius feature metric. Results show that the algorithm reliably extracts radius information from point clouds representing piping. In conjunction with the algorithm, the low-cost range camera hardware was able to characterize pipes, of radius ranging from 2.41 cm to 8.78 cm at a distance from the sensor ranging from 0.5 m to 3.75 m, with an average error of 18% for Kinect 1 and 10% for Kinect 2.
机译:在重型建筑行业中,线轴组件的几何形状很复杂,次优的制造工艺不可避免地会导致制造错误和昂贵的返工。为了减轻制造风险并提高产品质量,正在开发计算机辅助工具以提供附加的控制层。在本文中,研究了两个低成本测距相机的数据采集功能,以最终在智能制造设施环境中监控管道装配过程。各种管道的距离图像是在距传感器不同距离处系统地拍摄的。通过使用提出的半径估计算法,可以通过半径特征量度来评估用于精确几何管道特征检测的数据的实用性。结果表明,该算法能够可靠地从代表管道的点云中提取半径信息。结合该算法,低成本测距相机硬件能够表征半径为2.41 cm至8.78 cm的管道,并且距传感器的距离为0.5 m至3.75 m,其平均误差为18%。 Kinect 1和Kinect 2的10%。

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