中文摘要：

为在扫描数据的激光的特征察觉的方法自从技术的出现被学习十年了。然而，它仍然是在在不均匀的取样的点由于质地和结构信息抽取的困难处理的激光雷达数据的未解决的问题之一。纸在激光雷达数据为结构察觉分析 Gaussian (日志) 过滤器和它的潜在的使用的拉普拉斯算符的特征。基于木头，过滤在未组织的点上被介绍并且试验的一个特征察觉方法。方法过滤举起值(也就是， z 坐标价值) 由用在它的本地区域以内的日志核的卷绕旋转的每个点并且导出建议地面反对 / 展示的某些类型的存在的模式。实验被继续从一个邻居区域获得的点云数据集。结果证明在在定义的标准差之间的不同规模和关系检测的模式记载核和邻居尺寸，它指定被分析的本地区域。

英文摘要：

Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology. However, it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points. The paper analyzes the characteristics of Laplacian of Gaussian （LOG） Filter and its potential use for structure detection in LiDAR data. A feature detection method based on LoG filtering is presented and ex- perimented on the unstructured points. The method filters the elevation value （namely, z coordinate value） of each point by convo- lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob- jects/features. The experiments are carried on a point cloud dataset acquired from a neighborhood area. The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size, which specifies the local area that is analyzed.