Segmentation Clustering Algorithm for Point Cloud Processing of Targets on The Ground

Abstract

After the ground extraction algorithm, there are multiple objects, including the target in the non-ground point cloud. To effectively extract the features of individual objects and overcome the dependence of the traditional algorithm based on the normal vector and curvature of the region growing point cloud segmentation method on the initial point, the normal vector angle in the neighborhood, and the curvature threshold selection. In this paper, we propose a fast clustering segmentation method for region-growing point clouds based on neighborhood density. The test results show that the point cloud segmentation algorithm proposed in this paper has better performance than the traditional Euclidean clustering point cloud segmentation algorithm and region growing point cloud segmentation algorithm in terms of timeliness and correct segmentation rate. This new segmentation clustering algorithm can quickly implement automatic point cloud clustering segmentation of ground objects.