面向车载激光扫描点云快速分类的点云特征图像生成方法

A Classification-oriented Method of Feature Image Generation for Vehicle-borne Laser Scanning Point Clouds

以车载激光点云数据为研究对象,提出一种适合于其快速分类与目标提取的点云特征图像生成方法。首先将扫描区域进行平面规则格网投影,通过分析格网内部点云的空间分布特征(平面距离、高程差异、点密集程度等)确定激光扫描点的定权,从而生成车载激光扫描点云的特征图像。利用生成的点云特征图像,可采用阈值分割、轮廓提取与跟踪等手段提取图像分割的建筑物目标的边界,从而确定边界内部点云数据,实现目标分类与提取。本文以Optech公司的车载激光扫描数据为试验对象,验证本文提出方法的可行性和实用性。

An efficient method of feature image generation of point clouds to automatically classify dense point clouds into different categories is proposed, such as terrain points, building points. The method first uses planar projection to sort points into different grids, then calculates the weights and feature values of grids according to the distribution of laser scanning points, and finally generates the feature image of point clouds. Thus, the proposed method adopts contour extraction and tracing means to extract the boundaries and point clouds of man-made objects （e.g. buildings and trees） in 3D based on the image generated. Experiments show that the proposed method provides a promising solution for classifying and extracting man-made objects from vehicle-borne laser scanning point clouds.