Precise Registration of Scattered Cloud Data Based on the Particle Swarm Optimization
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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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Keywords:
- scattered cloud data /
- cross products of normal vector /
- PSO /
- re gistration
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