激光与光电子学进展, 2017, 54 (1): 011003, 网络出版: 2017-01-17   

基于曲率特征的迭代最近点算法配准研究 下载: 821次

Iterative Closest Point Algorithm Registration Based on Curvature Features
作者单位
中国矿业大学环境与测绘学院, 江苏 徐州 221116
摘要
在三维激光扫描技术中, 点云数据配准技术直接影响后期建模质量。点云配准主流算法为迭代最近点(ICP)算法, 该算法能自动、高精度配准, 也具有时间空间复杂度较大、收敛缓慢、易匹配错误对应点等缺点。将基于曲率极值的算法与ICP算法相结合, 对曲率特征明显的点云模型进行配准。从算法收敛效率、抗噪性及点云初始位置优劣对算法的影响三方面设计实验, 并与经典ICP算法及其他改进算法进行对比。结果表明, 该算法对于曲率变化明显的点云数据表现出的收敛效率高于其他算法, 对于质量较差的初始数据, 该算法收敛稳定性较强。
Abstract
Point cloud registration plays an important role in three-dimensional laser scanning technology as it affects modeling quality directly. The iterative closest point (ICP) algorithm is widely used in point cloud registration because it can register the point cloud automatically and accurately. But the ICP algorithm is complex in time and space, slow convergence and easy incorrect matching. The ICP algorithm and the curvature extremum algorithm are combined as a new algorithm to process point clouds with apparent curvature features. Experiments are conducted concerning effect of convergence efficiency, robustness and quality of initial data on the new algorithm, and the results of the classic ICP algorithm and other modified ICP algorithms are compared. The results show that the proposed algorithm has high convergence efficiency for point clouds with apparent curvature features and good convergence stability for worse initial data.

曾繁轩, 李亮, 刁鑫鹏. 基于曲率特征的迭代最近点算法配准研究[J]. 激光与光电子学进展, 2017, 54(1): 011003. Zeng Fanxuan, Li Liang, Diao Xinpeng. Iterative Closest Point Algorithm Registration Based on Curvature Features[J]. Laser & Optoelectronics Progress, 2017, 54(1): 011003.

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