Abstract: Point cloud segmentation is an important part of airborne LiDAR point clouds processing. The regional growth method is a traditional classical method of point cloud segmentation, but it usually takes the point as the unit to grow, which leads to the problems of slow segmentation speed and unstable segmentation performance. To solve these problems, this paper proposes a point cloud optimization fast segmentation algorithm combining K-means clustering method and regional growth method. First, K-means clustering is carried out for point cloud to obtain object primitives and calculate centroid points, judge whether the centroid points of each object element meet the angle and height difference threshold, and realize point cloud filtering based on centroid points. Then, the ground object primitives are traversed, and the normal vector angle and distance are calculated for the adjacent points within the object primitives to determine whether they meet the growth conditions of the regional growth threshold. The iteration is repeated until the end of the segmentation. Three groups of point cloud data from different regions are used for experimental analysis. The experimental results shows that the segmentation accuracy of this method could reach 86.19%, which is greatly improved compared with the traditional K-means clustering method and regional growth method airborne LiDAR point cloud segmentation accuracy. In addition, this method can significantly improve the computational efficiency compared with the traditional regional growth method.

Key words: airborne LiDAR, point cloud segmentation, object primitive, K-means clustering, regional growth