With the application of 3D point cloud data in 3D modeling, mapping, intelligent city and machine vision, point cloud data processing has become a research hotspot. Point cloud segmentation is the process of dividing the scattered point cloud data into more coherent subsets through a series of algorithms, which can provide the corresponding data base for the subsequent data analysis. To solve the problem that RANSAC algorithm is not effective in the segmentation of noisy and irregular point cloud data, an improved RANSAC point cloud segmentation algorithm is proposed. In this algorithm, KD tree is constructed, the selection method of initial point is redefined by using the spatial density of radius, the non-feature points are eliminated by multiple iterations, and the noise points are removed at the same time of point cloud segmentation; at the same time, the algorithm resets the judgment criteria, optimizes the combination of patches, and realizes the accurate segmentation of point cloud. The experimental results show that the improved RANSAC point cloud segmentation algorithm is a more effective point cloud segmentation algorithm, which has a larger amount of point cloud feature extraction data and a higher accuracy than Euclidean cluster segmentation algorithm.
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赵夫群,马玉,戴翀. 基于改进随机抽样一致的点云分割算法[J]. 科学技术与工程, 2021, 21(22): 9455-9460. Zhao Fuqun, Ma Yu, Dai Chong. Point Cloud Segmentation Algorithm Based on Improved RANSAC[J]. Science Technology and Engineering,2021,21(22):9455-9460.