Application of Improved DBSCAN Clustering Method in Point Cloud Data Segmentation

Point cloud data segmentation is the main task of point cloud data processing and it is also the prerequisite and key link for realizing automatic recognition of features. A laser point cloud data segmentation method based on the density clustering method is proposed in this paper and is verified in airborne cloud data segmentation. The method is an unsupervised clustering technique and the experiments show that this method has the advantages o multi-dimensional, good segmentation effect and high robustness.