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3D Laser Scanning Pointcloud Processing Uncertainty Estimation for Fuel Tank Volume Calibration
MAPAN ( IF 1 ) Pub Date : 2020-02-11 , DOI: 10.1007/s12647-020-00367-4
Mindaugas Knyva , Vytautas Knyva , Asta Meškuotienė , Pranas Kuzas , Darius Gailius , Žilvinas Nakutis

The implementation of modern 3D scanning method for horizontal fuel tanks’ calibration applications proposes versatility and advantages over the currently used volumetric method which requires using of liquid filler (in most cases water, and in some countries—gasoline). However, the data scatter in the 3D pointcloud deteriorates the accuracy of the volume calculation algorithm output and therefore increases the fuel tank volume measurement uncertainty. In this paper, the preprocessing method of the 3D pointcloud data is defined and the application for calibration of the horizontal fuel tanks is discussed. The uncertainty analysis and estimation of the horizontal fuel tank calibration is performed in this work implementing 3D pointcloud FIR filtering and regression preprocessing techniques. The mathematical model of the volume measurement and graduation was estimated, and the uncertainty sources were identified. The horizontal fuel tank graduation table is developed, and fragments of the calibration results using the scanning data from four fuel tanks with recommendations for the 3D pointcloud data processing are presented in this work.



中文翻译:

燃油箱容积校准的3D激光扫描Pointcloud处理不确定性估计

对于水平油箱的校准应用而言,现代3D扫描方法的实施提出了通用性和优势,优于目前使用的体积法,后者需要使用液体填充剂(在大多数情况下为水,在某些国家为汽油)。但是,3D点云中的数据分散会降低体积计算算法输出的准确性,因此会增加燃油箱体积测量的不确定性。本文定义了3D点云数据的预处理方法,并讨论了水平油箱校准的应用。在这项工作中,通过实施3D点云FIR滤波和回归预处理技术,进行了水平油箱校准的不确定性分析和估计。估算了体积测量和分度的数学模型,并确定了不确定性来源。制定了水平油箱刻度表,并在这项工作中介绍了使用来自四个油箱的扫描数据以及3D点云数据处理建议的校准结果片段。

更新日期:2020-04-23
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