Ordóñez Celestino, Cabo Carlos, Sanz-Ablanedo Enoc
Departmento de Explotación de Minas, Grupo de Investigación en Geomática y Computación Gráfica (GEOGRAPH), Universidad de Oviedo, 33004 Oviedo, Spain.
Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, Avenida de Astorga, s/n, 24001 Ponferrada, Spain.
Sensors (Basel). 2017 Jun 22;17(7):1465. doi: 10.3390/s17071465.
Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%.
移动激光扫描（MLS）是一项现代且强大的技术，能够在短时间内获取物体的大量点云数据。尽管这项技术如今在城市制图和三维城市建模中得到了广泛应用，但它仍存在一些缺点，需要加以改进以增强其性能。MLS数据最重要的缺点之一在于它提供的是一个无结构的数据集，其处理过程非常耗时。因此，人们越来越关注开发从MLS点云自动提取有用信息的算法。这项工作专注于建立一种方法并开发一种算法，用于使用MLS数据集检测杆状物体并将其分类为几个类别。所开发的过程首先通过体素化对该点云进行离散化，以简化并减少分割过程中的处理时间。相应地，开发了一种启发式分割算法，用于在MLS点云中检测杆状物体。最后，使用线性判别分析和支持向量机这两种监督分类算法来区分点云中不同类型的杆。预测变量是从激光点的笛卡尔坐标获得的主成分特征值、Z坐标范围以及一些与形状相关的指标。该方法在一个包含123根不同类别的杆的市区进行了测试。获得了非常令人鼓舞的结果，因为准确率超过了90%。
