中文摘要：

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。

英文摘要：

As one of the core steps in point cloud data processing, the registration result has great in- fluences on the subsequent data operations. Traditional precise registration methods mainly depend on artificial targets and feature points. These methods are limited by the external environment, initial conditions, feature points are not easy to find and so on. To overcome the limitation, this paper pro- poses an improved Particle Swarm optimization （PSO） algorithm. Using the sum of normal vectors~ cross products to define the fitness function, the current algorithm applies an efficient ＂Universal Search＂ and implements scattered cloud data registration based on the best registration points. By the experiment with the cloud data received by a multi-station scanning of a high steep slope rock and comparing the result with the classical algorithms such as ICP, the improved PSO algorithm is proved to be feasible, efficient and stable. It can effectively solve the problem of the targets or the feature points are not easy to find in registration process.