Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Department of Earth Science and Technology, City College, Kunming University of Science and Technology, Kunming 650233, China.
Sensors (Basel). 2022 Jul 6;22(14):5072. doi: 10.3390/s22145072.
With the development of societies, the exploitation of mountains and forests is increasing to meet the needs of tourism, mineral resources, and environmental protection. The point cloud registration, 3D modeling, and deformation monitoring that are involved in surveying large scenes in the field have become a research focus for many scholars. At present, there are two major problems with outdoor terrestrial laser scanning (TLS) point cloud registration. First, compared with strong geometric conditions with obvious angle changes or symmetric structures, such as houses and roads, which are commonly found in cities and villages, outdoor TLS point cloud registration mostly collects data on weak geometric conditions with rough surfaces and irregular shapes, such as mountains, rocks, and forests. This makes the algorithm that set the geometric features as the main registration parameter invalid with uncontrollable alignment errors. Second, outdoor TLS point cloud registration is often characterized by its large scanning range of a single station and enormous point cloud data, which reduce the efficiency of point cloud registration. To address the above problems, we used the NARF + SIFT algorithm in this paper to extract key points with stronger expression, expanded the use of multi-view convolutional neural networks (MVCNN) in point cloud registration, and adopted GPU to accelerate the matrix calculation. The experimental results have demonstrated that this method has greatly improved registration efficiency while ensuring registration accuracy in the registration of point cloud data with weak geometric features.
随着社会的发展，为了满足旅游、矿产资源和环境保护等需求，对山区和森林的开发利用也在不断增加。在野外勘测大场景时涉及的点云配准、三维建模和变形监测已成为许多学者的研究重点。目前，户外地面激光扫描（TLS）点云配准存在两个主要问题。首先，与城市和乡村中常见的具有明显角度变化或对称结构的强几何条件（如房屋和道路）相比，户外 TLS 点云配准大多采集具有粗糙表面和不规则形状的弱几何条件的数据，这使得将几何特征作为主要配准参数的算法无法控制对齐误差。其次，户外 TLS 点云配准的特点通常是单个站点的扫描范围大，点云数据庞大，这降低了点云配准的效率。针对上述问题，本文采用 NARF+SIFT 算法提取具有更强表达能力的关键点，扩展了多视图卷积神经网络（MVCNN）在点云配准中的应用，并采用 GPU 加速矩阵计算。实验结果表明，该方法在保证弱几何特征点云数据配准精度的同时，大大提高了配准效率。
