Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Application Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province, Kunming 650093, China.
Sensors (Basel). 2022 Dec 8;22(24):9627. doi: 10.3390/s22249627.
As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry. However, there are some differences in the quantity, quality, and information type of point clouds obtained by different methods when collecting point clouds of the same structure, due to differences in sensor mechanisms and collection paths. Thus, this study aimed to combine the complementary advantages of multi-source point cloud data and provide the high-quality basic data required for structure measurement and modeling. Specifically, low-altitude photogrammetry technologies such as hand-held laser scanners (HLS), terrestrial laser scanners (TLS), and unmanned aerial systems (UAS) were adopted to collect point cloud data of the same special-shaped structure in different paths. The advantages and disadvantages of different point cloud acquisition methods of special-shaped structures were analyzed from the perspective of the point cloud acquisition mechanism of different sensors, point cloud data integrity, and single-point geometric characteristics of the point cloud. Additionally, a point cloud void repair technology based on the TLS point cloud was proposed according to the analysis results. Under the premise of unifying the spatial position relationship of the three point clouds, the M3C2 distance algorithm was performed to extract the point clouds with significant spatial position differences in the same area of the structure from the three point clouds. Meanwhile, the single-point geometric feature differences of the multi-source point cloud in the area with the same neighborhood radius was calculated. With the kernel density distribution of the feature difference, the feature points filtered from the HLS point cloud and the TLS point cloud were fused to enrich the number of feature points in the TLS point cloud. In addition, the TLS point cloud voids were located by raster projection, and the point clouds within the void range were extracted, or the closest points were retrieved from the other two heterologous point clouds, to repair the top surface and façade voids of the TLS point cloud. Finally, high-quality basic point cloud data of the special-shaped structure were generated.
作为获取异形结构几何信息的最佳手段之一，点云数据采集可以通过激光扫描或摄影测量来实现。然而，由于传感器机制和采集路径的差异，用不同方法采集同一结构的点云时，所获得的点云在数量、质量和信息类型上存在一定的差异。因此，本研究旨在结合多源点云数据的互补优势，为结构测量和建模提供高质量的基础数据。具体来说，采用手持激光扫描仪（HLS）、地面激光扫描仪（TLS）和无人机系统（UAS）等低空摄影测量技术，从不同路径采集异形结构的点云数据。从不同传感器的点云采集机制、点云数据完整性和点云单点几何特征等方面，分析了异形结构不同点云采集方法的优缺点。此外，根据分析结果，提出了一种基于 TLS 点云的点云空洞修复技术。在统一三个点云的空间位置关系的前提下，对三个点云进行 M3C2 距离算法，从结构的同一区域提取出具有显著空间位置差异的点云。同时，计算了同邻域半径内多源点云的单点几何特征差异。利用特征差异的核密度分布，从 HLS 点云和 TLS 点云中融合特征点，丰富 TLS 点云中的特征点数。此外，通过栅格投影定位 TLS 点云空洞，并提取空洞范围内的点云，或者从另外两个异类点云中检索最近点，修复 TLS 点云的顶部表面和立面粉碎空洞。最终生成高质量的异形结构基础点云数据。
