摘要
针对目前储罐在位体积测量需求大、移位测量困难的问题,该文提出一种基于MVSNet多视角立体深度学习的储罐在位体积测量方法。首先,提出面向在位储罐体积测量的MVSNet深度预测改进多视图立体视觉方法,结合基于增量式运动恢复结构的储罐显著特征稀疏重建与相机姿态计算技术、基于MVSNet深度学习深度预测技术,获得储罐体积测量关键结构的稠密三维点云;然后,提出基于立体几何拟合在位储罐体积测量方法,旋转储罐点云与地面配准,并基于法线信息双阈值约束点云拟合储罐圆形拓扑结构,实现储罐体积测量。在2种储罐上进行初步实验,结果表明:该文方法提取到的高质量储罐点云数量比经典COLMAP框架分别增加15.6%、13.2%,点云提取时间分别缩短34.7%、39.2%,满足储罐在位体积测量需求。
For the current storage tank in-position volume measurement demand is large,but the measurement efficiency is low.This paper proposes an in-situ volume measurement technique for storage tanks,MVSNetbased multi-view stereo deep learning.Firstly,the MVSNet depth prediction improved multi-view stereo vision technique for in-situ tank volume measurement is proposed.It combining the incremental structure from motion-based tank salient features sparse reconstruction and camera pose calculation technique,and the MVSNet depth learning based depth prediction technique,the dense 3D point cloud of the key structure of the tank volume measurement is obtained.Secondly,an in-situ tank volume measurement technique based on stereometric fitting is proposed.It rotates the tank point cloud to make the ground alignment,and fit the tank circular topology based on the normal information double threshold constrained point cloud,in order to achieve the tank volume measurement.Preliminary experiments are conducted on two types of tanks,the results show that,the number of high-quality tank point clouds,which is extracted by this paper,increases by 15.6% and13.2% respectively compared with that of the COLMAP framework.The point cloud extraction time was reduced by 34.7% and 39.2%,respectively.This method meets the in-situ tank volume measurement requirements.
作者
刘桂雄
肖天歌
陈国宇
黄坚
LIU Guixiong;XIAO Tiange;CHEN Guoyu;HUANG Jian(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China;Guangzhou Institute of Energy Testing,Guangzhou 511447,China;Guangzhou Institute of Measurement and Testing Technology,Guangzhou 510663,China)
出处
《中国测试》
CAS
北大核心
2023年第1期26-30,49,共6页
China Measurement & Test
基金
广东省自然科学基金-面上项目(2020A1515010947)
广州市科技计划项目(202002030439)
广东省重点领域研发计划项目(2019B010154003)。
