改进蚁群算法的三维激光点云聚类方法

doi:10.13474/j.cnki.11-2246.2018.0072

测绘通报 ›› 2018, Vol. 0 ›› Issue (3): 38-42.doi: 10.13474/j.cnki.11-2246.2018.0072

改进蚁群算法的三维激光点云聚类方法

江珊, 吕京国, 李现虎

北京建筑大学测绘与城市空间信息学院, 北京 102616

收稿日期:2017-10-11 修回日期:2018-01-16 出版日期:2018-03-25 发布日期:2018-04-03
作者简介:江珊(1992-),女,硕士生,主要从事摄影测量与遥感方面的研究。E-mail:jiangshan@stu.bucea.edu.cn
基金资助:
北京市教育委员会科研项目（03058314105）；北京市教育委员会科研能力提升计划（03058216001）；北京城市空间信息工程重点实验室资助（2017212）；贵阳人防大数据应用工程技术研究中心开放基金；北京建筑大学研究生创新项目（PG2017016）

3D Point Cloud Clustering Based on Improved Ant Colony Algorithm

JIANG Shan, LÜ Jingguo, LI Xianhu

School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

Received:2017-10-11 Revised:2018-01-16 Online:2018-03-25 Published:2018-04-03

摘要/Abstract

摘要：

为了提高点云聚类方法的效率和精度，本文提出了一种蚁群优化投影寻踪算法。试验采用机载LiDAR点云数据，通过构建蚁群算法中信息素系数更新的对数反正切函数模型来减少所需的信息素更新系数的迭代次数，不断优化的投影寻踪方向投影，提高寻找最佳投影方向的连续空间的效率，实现了树木和建筑的分割。试验使用人工方法对比评估树木和建筑物的位置和数量的准确性。

关键词: 点云聚类, 蚁群算法, 投影寻踪, 点云分割

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

中图分类号:

P237

江珊, 吕京国, 李现虎. 改进蚁群算法的三维激光点云聚类方法[J]. 测绘通报, 2018, 0(3): 38-42.

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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改进蚁群算法的三维激光点云聚类方法

3D Point Cloud Clustering Based on Improved Ant Colony Algorithm

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