华南理工大学学报(自然科学版) ›› 2009, Vol. 37 ›› Issue (9): 103-107.

• 机械工程 • 上一篇    下一篇

多传感器集成测量系统的数据对齐方法

刘鹏鑫 王扬   

  1. 哈尔滨工业大学 机电工程学院, 黑龙江 哈尔滨 150001
  • 收稿日期:2008-08-02 修回日期:2008-11-05 出版日期:2009-09-25 发布日期:2009-09-25
  • 通信作者: 刘鹏鑫(1979-),女,博士生,主要从事逆向工程、CAD/CAM研究. E-mail:pengxin—bird@163.com
  • 作者简介:刘鹏鑫(1979-),女,博士生,主要从事逆向工程、CAD/CAM研究.
  • 基金资助:

    国家自然科学基金资助项目(50675053)

Data Registration Method for Multiple-Sensor Integrated Measurement System

Liu Peng-xin  Wang Yang   

  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
  • Received:2008-08-02 Revised:2008-11-05 Online:2009-09-25 Published:2009-09-25
  • Contact: 刘鹏鑫(1979-),女,博士生,主要从事逆向工程、CAD/CAM研究. E-mail:pengxin—bird@163.com
  • About author:刘鹏鑫(1979-),女,博士生,主要从事逆向工程、CAD/CAM研究.
  • Supported by:

    国家自然科学基金资助项目(50675053)

摘要: 目前多传感器集成测量系统的数据对齐精度完全依赖于对几何参考体的测量精度,对齐的灵活性不高,过程比较复杂,为此文中提出了一种不依靠简单几何体作为基准的自动对齐算法.该算法首先通过计算点集曲率来辅助选取3对近似对应点,求解坐标变换矩阵实现初始对齐;然后对非接触扫描点云进行三角剖分,以接触式测量所获点集向对应三角片的投影寻找对应点,之后进行坐标变换迭代求解,从而实现了不存在对应点的点集之间的对齐;最后用包围盒求交法实现了数据的融合.实验结果表明,本算法对初始位置要求不高,在改善对齐精度的同时减少了对齐过程中的人工干预.文中数据对齐的最小二乘误差最大为0.045μm,最小为0.025μm.

关键词: 逆向工程, 多传感器集成测量系统, 迭代最近点, 数据对齐

Abstract:

At present,the precision of data registration of multiple-sensor integrated measurement system(MIMS) completely depends on the measurement accuracy of geomatric references,and the registration process is complex and not flexible.In order to solve these problems,an automatic registration algorithm is proposed which does not require any primitive artifacts as data.In this algorithm,first,the curvatures of point sets are calculated,and three pairs of points with similar curvature are selected to compute the transform matrix for an initial registration. Next, in order to obtain the corresponding points, the point sets obtained by contact measurement are projected to the triangular meshes constructed from the triangulation of non-contact scanning point cloud. Then, the coordinate transformation is achieved using a iterative method, which helps to implement the registration of the point sets without corresponding points. Finally, the intersection of bounding box is utilized to merge data into one set. Experimental resuits show that the proposed algorithm insensitive to the initial position improves the registration precision, reduces the manual intervention in the registration process and results in a maximum and a minimum least square registration errors of 0. 045 and 0. 025 μm, respectively.

Key words: reverse engineering, multiple-sensor integrated measurement system, iterative closest point, data registration