Planar Feature Extraction and Fitting Method Based on Density Clustering Algorithm

摘要

We adopt clustering algorithm to improve segmentation accuracy. In this paper, 3D laser scanning platform was built to obtain the spatial 3D point cloud data. And then we extracted the point cloud data for two planar features. K-means algorithm, density-based clustering algorithm and density peak clustering algorithm were employed to split the 3D point cloud of the two planes. After clustering, we compared and analyzed the clustering results of the three clustering algorithms. More importantly, we also found that for peak density clustering, the threshold value is related to its sensitivity to noise points. After fitting the two planes, the verticality of two planes was also calculated. We analyzed the results and summarized the criterion for selecting thresholds.