基于“一面两孔”特征的点云配准方法

Registration Method for Point Cloud Based on Feature of One Plane and Two Cylindrical Holes

为了提高零件在扫描检测过程中点云与设计模型的配准精度,提出了一种基于＂一面两孔＂特征的点云配准方法.该方法粗配准以零件的平面/圆柱孔特征为对象,使设计模型和点云的局部坐标系重合,并通过改进ICP算法求解点云与设计模型最近点的距离最小平方和实现精配准.由于配准区域和最近点的计算方法不同,精配准进一步分为全域和特征域配准两种类型.全域精配准以距点云最近的设计模型三角网格点或投影点为最近点,适合于毛坯件;特征域精配准则通过求解点云在平面/圆柱孔特征上的投影点为最近点,适合于成品件.试验及计算结果表明：全域配准的配准精度随表面离散点距离的减小而提高,当离散点距离达到1.50 mm时,其配准精度已经达到0.15 mm,基本满足工程应用要求.当配准精度相同时,配准效率较其它方法提高10%～20%。

For improving the registration accuracy between point cloud and design model of the parts during scan detection, a registration method based on the feature of one plane and two cylindrical holes was proposed. The rough registration applies the features of plane and holes to coincide with the local coordinate systems of point cloud and design model. The fine registration is finished by using the improved iterative closest point （ICP） algorithm to calculate the sum of least squares of the distance between the closest points and point cloud. Since the calculation methods of registration areas and the closest points are different, fine registration is further classed into the global domain and feature domain types. The closest points of global registration are obtained by comparing the distance between point cloud and the nearest design model grid points or projection points, which is suitable for blank parts. The projection points of point cloud on a plane or a cylindrical hole are the closest points of feature domain registration, which is suitable for finished parts. The test results show that the accuracy of global registration will increase with the distance the distance reaches 1.50 mm, the accuracy is requirements. For the same registration accuracy, compared with that of other methods. between surface discrete points decreasing. When about 0. 15 mm, which meets the engineering the efficiency will be improved by 10%-20 % compared with that of other methods.