LIDAR Automatic Feature Detection and Object Classification

激光雷达（光探测和测距）是一种相对较新的技术，用于快速准确地扫描地球表面。 由此产生的几何模型可以应用于许多领域的关键应用，包括城市规划，管理和可视化;安全;更新和维护地籍（地产线）数据;景观变化检测;虚拟现实;和操作使命规划。 在大多数相关应用中，地理空间特征（本文中简称为特征）或诸如建筑物、植被、道路和水道之类的物体的描绘和识别是主要关注的。 由于传统的手动提取方法成本高且耗时，并且从不同来源收集大量LiDAR数据，因此对自动LiDAR特征提取的需求不断增长[卡里2009]。 然而，必须注意的是，工业环境中的LiDAR数据集规模（可能达到数十亿个点）带来了巨大的性能挑战，甚至超出了最先进的已发表研究技术的范围。

LiDAR (LIght Detection And Ranging) is a relatively new technology for scanning the earth's surface quickly and accurately. The resulting geometry models can be applied to crucial applications in many fields, including urban planning, management, and visualization; security; updating and maintaining cadastral (property line) data; landscape change detection; virtual reality; and operations mission planning. In most relevant applications, the delineation and identification of geospatial features (herein referred to simply as features), or objects such as buildings, vegetation, roadways and waterways, are of primary interest. Since traditional manual extraction methods are costly and time consuming, and large quantities of LiDAR data are being gathered from different sources, there is a growing demand for automatic LiDAR feature extraction [Cary 2009]. However, it must be noted that the scale of LiDAR datasets (potentially billions of points) found in industrial settings introduces formidable performance challenges, beyond the scope of even the most elegant published research techniques.