Point Cloud Key Point Extraction Method Based on Curvature Threshold and Clustering Analysis
In order to solve the problem that traditional 3D scanning is vulnerable to the interference of parts sticking and scan-ning noise in extracting point cloud data,and the computation cost is too large,a method of point cloud key point extraction based on curvature threshold and clustering analysis is proposed.First,a method combining statistical filtering and radius filtering is pro-posed to preprocess the point cloud data and avoid the interference of noise points.Subsequently,the approximate curvature of dis-crete points is calculated and the statistical curvature distribution is used to select the threshold.Then,the point cloud density is an-alyzed by clustering algorithm to determine the cluster center,which is also the most obvious point.Finally,the calibration algo-rithm is used to achieve the calibration of the clustering center and the original point cloud data to complete the key point extraction for the part contour.A series of experimental results show that this method can achieve the extraction of key points of parts quickly and efficiently without much human intervention in parameter adjustment,and has high accuracy.
key point extractionpreprocessingdata analysiscurvature thresholdclustering analysis
万方数据
维普
