An Efficient Class-Constrained DBSCAN Approach for Large-Scale Point Cloud Clustering

To better interpret the scene and facilitate the subsequent processing of large-scale point cloud, clustering is often implemented in the preprocessing stage. However, when the original density-based spatial clustering of application with noise (DBSCAN) approach is used for point cloud clustering, it is easy to categorize closely spaced vegetation points and nonvegetation points into the same cluster by mistake. Aiming at the problem, this article presents an improved DBSCAN by embedding a strategy of class constraint, which is called CC-DBSCAN. Specially, based on the RGB and label information of each point in the training samples, by using the logistic regression model, the logistic regression color index (LRCI) is calculated for each point in the clustering samples. Then, points to be clustered are classified as vegetation points and nonvegetation points through the LRCI. Furtherly, the class information of point is introduced as a constraint for ensuring the core point and its directly density-reachable points belong to the same class, thus, solving the problem that confusion cluster of the adjacent vegetation points and nonvegetation points. We evaluate our approach on the benchmark SensatUrban dataset, where Cambridge_28 scene dataset is taken as the training set and Cambridge_18 scene dataset is as the dataset to be clustered. Experimental results show that our method achieved 97.20% purity of point cluster, which outperforms the other DBSCAN methods. At the same time, it takes only 24.25 s for clustering 2 million points, which indicates that CC-DBSCAN has high computational efficiency and good practicability.