通过对三维激光扫描技术在铁路罐车中的应用研究，对扫描过程中发现的点云残缺及噪点问题进行研究分析，提出一种快速有效点云优化处理方法，新方法包括sp-H点云预处理和Eti-G建模优化。验证试验表明，采用新的点云优化处理方法可以在较短时间内将扫描点云进行去噪及残缺修补，并能够快速高效进行模型重建，提升了不同工况环境下扫描点云的适用性，提高了扫描工作效率和准确度，铁路罐车容积测量结果的相对扩展不确定度达到2.4×10−3，为铁路罐车容积扫描最高准确度等级，为三维激光扫描相关技术的发展提供一定借鉴。
Abstract
Based on the application research of 3D laser scanning technology in railway tank car, the problems of incomplete point cloud and noise which were found during the scanning process were analyzed, and a fast and effective optimized processing method of point cloud was proposed. The new method included sp-H point cloud pre-processing method and Eti-G modeling optimization key algorithm. The verification test results show that the new optimized processing method of point cloud can be used to optimize the noise and incomplete point cloud in a relatively short time and can fast and efficiently reconstruct the model, which improve the applicability of scanning the point cloud under different working conditions, and the efficiency and accuracy of scanning work are also promoted. The related expanded uncertainty of volume measurement results of railway tank car reaches 2.4×10−3, which is the highest accuracy level of railway tank car volume scanning, and provides some references for the development of 3D laser scanning technology.
关键词
铁路罐车 /
噪点 /
点云 /
优化 /
三维激光扫描
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Key words
railway tank car /
3D laser scanning /
point cloud /
noise /
optimization
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基金
铁道部科技研究开发计划项目（2011Z009-A）
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脚注
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