基于特征点和改进ICP的三维点云数据配准算法  

Research of 3D point cloud data registration algorithms based on feature points and improved ICP

作　　者：张晓娟[1] 李忠科[1] 王先泽[1] 吕培军[2] 王勇[2] 

机构地区：[1]第二炮兵工程学院指挥自动化系,陕西西安710025 [2]北京大学口腔医学院,北京100871

出　　处：《传感器与微系统》2012年第9期116-118,122,共4页Transducer and Microsystem Technologies

基　　金：国家科技支撑计划资助项目(2009BAI81B00)

摘　　要：在光学非接触三维测量中,复杂对象的重构需要多组测量数据的配准。最近点迭代(ICP)算法是三维激光扫描数据处理中点云数据配准的一种经典的数学方法,为了获得更好的配准结果,在ICP算法的基础之上,提出了结合基于特征点的等曲率预配准方法和邻近搜索ICP改进算法的精细配准,自动进行点云数据配准的算法,经对牙齿点云模型实验发现,点云数据量越大,算法的配准速度优势越明显,采用ICP算法的运行时间(194.58 s)远大于本算法的运行时间(89.13 s)。应用实例表明:该算法具有速度快、精度高的特点,算法效果良好。In the optical non-contact 3D measurement process, the reconstruction of complex object depends on the registering of many point clouds. Iterative closest point（ICP） algorithm is a classical mathematical method in processing data of 3D laser scanning about registration. To obtain better registering result, an algorithm including initial registration and precise registration is proposed based on ICP. It combines algorithm of equal curvature based feature points and improved ICP of neighborhood search to register point cloud data automatically. Through the tooth point clouds model experiment, it is found that the greater the amount of point cloud data, the more obvious advantages of the registration speed of this algorithm is the running time of using ICP algorithm（ 194.58 s） is far greater than that of this algorithm （89.13 s）. Experimental results show that the proposed algorithm has advantage of fast speed and high precision.

关 键 词：点云 配准 特征点 最近点迭代算法 牙齿点云模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]