Abstract
Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.
Highlights
Light Detection and Ranging (LIDAR) is an important means of obtaining building data
This paper proposes the AQ-Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which is a density clustering segmentation method combined with Gaussian mapping
Focusing on the advantages and disadvantages of the above methods, this paper proposes AQ-DBSCAN, an algorithm that improves upon the DBSCAN algorithm by including an automatic segmentation method for point cloud data obtained from buildings
Summary
Light Detection and Ranging (LIDAR) is an important means of obtaining building data. Method, the cluster is a high-density object region separated by a low-density region in data space, and the data in the sparse data region is considered as noise data This algorithm sets a certain threshold, and as long as the density of the adjacent area of a certain point exceeds the threshold, the cluster can proceed. This algorithm proposed a method of determining the number of representative points according to the clustering dimension, which can reduce the number of iterations It still requires setting of the neighborhood radius manually and cannot be fully automated. Focusing on the advantages and disadvantages of the above methods, this paper proposes AQ-DBSCAN, an algorithm that improves upon the DBSCAN algorithm by including an automatic segmentation method for point cloud data obtained from buildings. 0.009347 0.057143 0.104939 0.152735 0.200532 0.248328 0.296124 0.34392 0.391716 0.439512 0.487309 0.535105 0.582901 0.630697 0.678493 0.72629 0.774086 0.821882 0.869678 0.917474 0.965271 1.013067 1.060863 1.108659 1.156455 1.204252 1.252048 1.299844 1.34764 1.395436 1.443232 1.491029 1.538825 1.586621 1.634417 1.682213 1.73001 1.777806 1.825602 1.873398 1.921194 1.968991 e radius of neighborhood
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