Adaptive Region Growing Method for Complex Surface Point Cloud Segmentation
Before the robot machining complex surface automatically,it needs to divide the area of the tar-get surface to fit different process parameters. Aiming at the over-segmentation and under-segmentation problems caused by using the existing method to segment complex surface point cloud,an adaptive region growing method for complex surface point cloud segmentation is proposed. Firstly,an adaptive growth crite-rion based on dynamic smoothness threshold is designed for point cloud segmentation of complex surface objects to reduce over-segmentation. Secondly,the under-segmentation criterion is proposed to judge the re-sult point clouds of segmentation and filter out the under-segmented regions. Finally,the under-segmented region is re-segmented until there is no more under-segmented region. Taking the complex surface point cloud obtained by scanning ceramic sanitary ware blank as the experimental object,the segmentation effect of the proposed method on complex surface point cloud was tested. Experimental results show that the seg-mentation precision of the proposed method is up to 92.63％,the recall rate is up to 98.89％,and the F1-score of the comprehensive evaluation index is up to 95.66,and each region of complex surface point cloud can be segmented effectively and completely.
万方数据
维普
