Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

中文翻译：

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一种新的自动点云注册关键点匹配算法的实验研究

安装在移动或固定平台上的光检测和测距（LiDAR）数据系统可为各种目的提供3D点云数据。在需要对某个区域或物体进行两次或更多次测量的应用中，获得的点云的精确配准对于通过重叠点云的组合生成健康模型至关重要。使用迭代最近点（ICP）算法或其变体在公共坐标系中自动注册点云是文献中经常使用的方法之一，并且许多研究着重于改进注册过程算法以获得更好的结果。 。这项研究提出并测试了一种不同的方法，该方法可以在使用ICP算法进行精细配准之前，对点云的粗略配准进行自动关键点检测和匹配。在提出的算法中，考虑到关键点之间的几何关系，通过关键点之间的角度和距离表示匹配。因此，我们的目标是通过ICP方法执行通过精细配准过程获得的3D模型的质量改进。使用3D变换在粗略对齐阶段的均方根误差（RMSE）以及ICP算法的事前和事后RMSE值来评估新算法的性能。还将该新算法与点特征直方图（PFH）描述符和匹配算法进行了比较，随附两个常用的探测器。比较结果讨论了该算法的优缺点。实验中使用的数据集的测量是在实验室环境中使用6 cm×6 cm×10 cm亚里士多德雕塑的扫描数据，室外的建筑物立面以及公开可用的斯坦福兔子雕塑数据进行的。在每个案例研究中，与其他粗略配准方法相比，所提出的算法在ICP过程中提供了令人满意的性能，更高的精度和更少的迭代次数。从使用建议的方法进行粗注册的点云中，根据RMS迭代的RMSE值的精注册准确度被计算为〜0。