为了实现具有不同测量尺度、分辨率与精度等特性的跨源点云的精确配准,提出了基于多尺度采样的测量点云数据配准方法。通过尺度滑移算法来滤除高频细节信息,保留轮廓数据,并结合体素网格邻域法来实现点云数据的降采样;对于宏观结构光视觉测量的低分辨率点云数据,通过基于深度学习的渐进式三维点云上采样算法可以精确还原结构光点云的轮廓细节,实现跨源点云在尺度与分辨率上的统一。最后,采用迭代最近点法对处理后尺度近似的数据进行配准,并将配准关系逆向用于原始跨源点云的配准。实验结果表明,多尺度采样方法对于跨源点云的配准精度有所提高,能有效用于发动机叶片等零部件的高性能检测。

In order to realize the accurate registration of cross-source point clouds with different measurement scales, resolutions, and accuracy, a measurement point cloud data registration method based on multi-scale sampling is proposed. The scale slip algorithm is used to filter out the high-frequency details, retain the contour data, and combine the voxel grid neighborhood method to realize the downsampling of point cloud data. For the low-resolution point cloud data measured by macro-structured light vision, through the progressive three-dimensional point cloud upsampling algorithm based on depth learning, the contour details of structured light point clouds can be accurately restored, and the unity of scale and resolution of cross-source point clouds can be realized. Finally, the iterative nearest point method is used to register the data with scale approximate after processing, and the registration relationship is inversely applied to the registration of the original cross-source point cloud. The experimental results show that the multi-scale sampling method can improve the registration accuracy of cross-source point clouds and can be effectively used for high-performance detection of engine blades and other parts.