韩贤权, 朱庆, 丁雨淋, 周东波. 散乱点云数据精配准的粒子群优化算法[J]. 武汉大学学报 ( 信息科学版), 2014, 39(10): 1214-1220.
引用本文: 韩贤权, 朱庆, 丁雨淋, 周东波. 散乱点云数据精配准的粒子群优化算法[J]. 武汉大学学报 ( 信息科学版), 2014, 39(10): 1214-1220.
HAN Xianquan, ZHU Qing, DING Yulin, ZHOU Dongbo. Precise Registration  of  Scattered Cloud Data Based on the Particle Swarm Optimization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1214-1220.
Citation: HAN Xianquan, ZHU Qing, DING Yulin, ZHOU Dongbo. Precise Registration  of  Scattered Cloud Data Based on the Particle Swarm Optimization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1214-1220.

散乱点云数据精配准的粒子群优化算法

Precise Registration  of  Scattered Cloud Data Based on the Particle Swarm Optimization

  • 摘要: 作为点云数据处理的关键步骤点云数据配准的结果直接影响后续数据处理的精度 基于人工标靶和ICP思想的传统配准方法存在受环境影响初始条件限制以及特征点提取困难等问题 针对传统地面激光扫描点云数据的高精度配准方法主要依赖人工标靶和特征点选取等局限提出了一种改进的粒子群优化算法以法向量叉积代数和最小作为适应度函数对相邻点云重叠区域内的所有数据进行高效的全局搜索在选取最佳配准点的基础上实现了散乱点云的精确配准 通过对多站扫描的高陡边坡岩体点云数据进行整体配准并与ICP等经典算法进行对比实验结果验证了本方法的可行性有效性和稳定性可以有效解决配准过程中标靶或同名特征点不易寻找的问题

     

    Abstract: As one of  the  core  ste ps in point  cloud  data processingthe  re gistration  result  has great  influences  on the  subsequent data operations.Traditional  precise  re gistration methods mainly depend on artificial  tar gets  and feature  points.These methods are  limited  by the  external  environmentinitial conditionsfeature  points  are  not eas y to  find  and so on.To overcome  the  limitation this paper  proposes  an  improved Particle Swarm optimization PSOal gorithm.Using the  sum of  normal  vectors cross  products  to define  the  fitness  function the  current  al gorithm applies  an efficient Universal Searchand  implements  scattered  cloud  data  re gistration  based on the best  re gistration  points.By the experiment with the  cloud  data  received by a multi-station  scanning of  a hi gh stee p slo pe rock and comparing the  result with the  classical  al gorithms  such as  ICPthe  improved PSO al gorithm is  proved to be  feasibleefficient  and stable.It  can effectivel y solve  the problem of  the  tar gets  or  the  feature points  are  not eas y to  find  in  re gistration  process.

     

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