一种改进的小组立工件图像处理算法
An Improved Image Processing Algorithm for Sub-Assembly
于航 1赵亦希 2康世豪3
作者信息
- 1. 上海船舶工艺研究所,上海 200032;上海交通大学,上海 200241
- 2. 上海交通大学,上海 200241
- 3. 上海船舶工艺研究所,上海 200032
- 折叠
摘要
针对三维相机拍摄得到的小组立复杂结构件点云数据,建立小组立焊接机器人的整体视觉算法系统.在处理点云图像时,发现阴影和常规点云滤波方法可能会破坏模型,因此采用离群滤波算法对点云数据进行清理,估计提取的特征向量,并结合曲率滤波算法去除小范围阴影噪声平面.经过设备调试和生产验证,成功剔除噪声点,并有效保留三维模型的尖锐特征,为后续点云配准和三维重建提供基础.整个小组立三维相机处理系统可涵盖大部分小组立复杂结构件,这一研究成果可为船厂小组立焊缝识别视觉系统的设计提供有益参考.
Abstract
An overall visual algorithm system for sub-assembly welding robots is established based on the point cloud data of the sub-assembly complex structural components captured by 3D cameras.When the point cloud images are processed,it is found that the shadows and conventional point cloud filtering methods may damage the model,so the outlier filtering algorithm is employed to clean the point cloud data,followed by the estimation of normal vectors based on extracted features,and the small-scale shadow noise planes are removed combined with the curvature filtering algorithm.Through equipment testing and production validation,the noise points are successfully eliminated and the sharp features of the 3D model are maintained,setting the stage for subsequent point cloud registration and 3D reconstruction.The entire sub-assembly 3D camera processing system can cover most complex structural components of the sub-assembly.The findings of this study offer valuable insights for the design of a visual system for vertical weld identification in shipyard sub-assembly teams.
关键词
小组立/图像数据增强/点云滤波/图像补全/曲率滤波Key words
sub-assembly/image data augmentation/point cloud filtration/image completion/curvature filtration引用本文复制引用
出版年
2024
NETL
NSTL
万方数据