Wu Jianing, Xiao Zhang, Chen Fan, Peng Tianlin, Xiong Zhi, Yuan Fengwei
School of Mechanical Engineering, University of South China, Hengyang 421001, China.
School of Wealth Management, Ningbo University of Finance & Economics, Ningbo 315000, China.
Sensors (Basel). 2022 Oct 2;22(19):7479. doi: 10.3390/s22197479.
With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.
随着三维点云数据量的增加以及点云配准在各个领域的广泛应用，能否快速提取配准关键点并进行精确的粗配准这一问题亟待解答。在本文中，我们提出了一种新颖的语义分割算法，该算法能使提取的特征点云具有聚类效果以实现快速配准。首先，提出了一种自适应技术来确定局部点的邻域半径。其次，通过由法向量计算得到的区域波动系数和平稳系数对点的特征强度进行评分，初步确定待配准的高特征区域。最后，使用FPFH描述提取的语义特征点云的几何特征，从而实现从局部点云到整体点云的粗配准。结果表明，基于语义特征的唯一性可以对该点云进行粗略分割。使用语义特征点云能使点云基于粗配准精度具有非常快的响应速度，几乎与使用原始点云时相同，这有利于快速确定初始姿态。
