点云
迭代最近点
高斯分布
图像配准
点(几何)
计算机科学
混合模型
职位(财务)
高斯网络模型
人工智能
过程(计算)
激光雷达
计算机视觉
算法
遥感
几何学
数学
图像(数学)
物理
地理
量子力学
财务
经济
操作系统
作者
Yongzhi Wang,Tao Zhou,Hui Li,Wenlong Tu,Jing Xi,Lixia Liao
标识
DOI:10.1080/01431161.2021.2022242
摘要
As a fundamental process of three-dimensional lidar point cloud data (3D LPCD) processing, numerous registration methods are time consuming and easily fall into local optimum. A 3D LPCD registration method based on the iterative closest point (ICP) algorithm, which is improved by the Gaussian mixture model (GMM) considering corner features, is proposed in this article to address these limitations. The GMM method is used for coarse registration, and the input original 3D LPCD is replaced by corner features extracted by the improved 3D Harris algorithm to improve the efficiency of coarse registration. In addition, a satisfactory initial position between the reference and the moving 3D LPCD is prepared for ICP fine registration by coarse registration; thus, the accuracy of fine registration can be improved. The registration accuracy and efficiency of the new method is proved to be higher than those of four common ICP-based registration methods (3DSC-RANSACICP, 3DSC-SAC-IAICP, FPFH-RANSACICP, and FPFH-SAC-IAICP), and GMM registration methods, and the local optimum problem is effectively addressed.
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