期刊文献+

无编码全局控制点多视角三维数据拼接 被引量:7

Multi-view 3D Data Registration Based on Global Control Codeless Points
下载PDF
导出
摘要 多视角三维数据的拼接一直是视觉测量领域研究的难点之一,本文提出了一种无编码全局控制点的多视角三维数据拼接方法。以多个固定不动的无编码标志点作为全局控制点,建立全局坐标系。移动结构光立体视觉测量系统的不同视角对物体表面各子区域进行测量,获得局部坐标系下的三维测量数据及控制点。根据控制点的空间几何变换不变性自动匹配出各视角局部控制点在全局控制点中的同名点。依据同名控制点对,采用SVD算法求解局部坐标系与全局坐标系的变换关系,从而实现全局拼接。最后,以10个圆形标志点为全局控制点从5个视角对鞋楦进行测量,实验结果表明:该方法消除了多视角拼接中的累积误差,控制点的重合度平均误差为η=0.013mm,与手持式扫描仪对比标准偏差为0.177 mm,获得了较好的拼接效果。 Multi-view 3D data registration is always a research challenges in vision measurement, and a multi-view of 3D data registration method based on global control codeless points is proposed. The global coordinate system is established with many fixed codeless marked points as global control points. The stereo vision combined with structured light measurement system is moved to different locations to measure the object surface of each region, and obtain the local coordinates of 3D data and control points. Automatically obtaining the pairs same named control points of the local and global based on the characteristics of spatial geometry transform invariance of these control points. According to the pairs of control points, SVD algorithm is adopted to solve the transformation relation from local coordinate system to global, and then the global registration is complete. Finally, with 10 circle marked points for the global control points, a shoe last is measured from five different views. The experimental results show that the method can eliminate the cumulative error in the process of multi-view 3D data registration. The average error of coincidence degree is 0.013 mm, the comparison of standard deviation with hand-held scanner is 0.177 mm, and the good registration result can be obtained..
出处 《光电工程》 CAS CSCD 北大核心 2014年第5期57-62,共6页 Opto-Electronic Engineering
基金 国家科技支撑计划资助项目(2012BAF12B15) 国家自然科学基金资助项目(51175191 51105150) 福建省自然科学基金资助项目(2011H2003 2013J01190 2011J01314) 中央高校基本科研业务费专项资金资助(JB-ZR1158)
关键词 多视角三维数据拼接 全局控制点 SVD算法 累积误差 multi-view 3D data registration global control points SVD algorithm cumulative error
  • 相关文献

参考文献11

二级参考文献65

共引文献84

同被引文献55

引证文献7

二级引证文献17

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部