3D Point Cloud Clustering Based on Improved Ant Colony Algorithm

doi:10.13474/j.cnki.11-2246.2018.0072

Abstract

Abstract:

In order to improve the efficiency and accuracy of the existing point cloud clustering method,an ant colony optimization projection pursuit is proposed.This method provides better clustering results with respect to airborne LiDAR point clouds.The paper constructs the log and arctan function model to reduce the number of iterations required to update pheromone coefficients in ant colony algorithms,continuously optimize projection on the projection pursuit direction,and improve the efficiency of searching for the best projection direction in continuous spaces,which realizes point cloud clustering for trees and buildings through the ant colony optimization projection.The accuracy of the location and the number of the trees and the building are evaluated using manual methods.

Key words: point cloud clustering, ant colony algorithm, projection pursuit, point cloud segmentation

CLC Number:

P237

JIANG Shan, LÜ Jingguo, LI Xianhu. 3D Point Cloud Clustering Based on Improved Ant Colony Algorithm[J]. 测绘通报, 2018, 0(3): 38-42.

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