期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B2
页码：156-159
出版社：Copernicus Publications
摘要：The paper focuses on a particular aspect of feature extraction from LiDAR data. To support transportation flow data estimation, points reflected back from vehicles should be extracted from a LiDAR cloud. A simple thresholding can certainly provide a good starting point to solve this task, but in order to achieve a robust solution there are several other tasks that should be addressed. First, the road itself should be identified (actually continuously followed) to define the search window for the vehicles. Then, the surface of the road must be modeled to obtain true elevation of the vehicle (which is measured in the normal direction of the surface). Once the LiDAR points representing a vehicle have been obtained, at minimum the vehicle orientation should be determined such as travel direction. This paper introduces a technique to accomplish the above mentioned tasks. The road is followed by the guidance of an initial coarse centerline description. Then a preprocessing phase takes place, the point cloud is segmented to get the vehicle blobs. The segmentation is based on standard image processing methods, such as histogram thresholding or edge detection techniques, both methods are currently under consideration. In the next step, vehicle outlines are created using statistical parameters, such as standard deviation of height values or height "texture" measures. The robustness of the process has been improved by using Delaunay- triangulation to test slope measures. The newly developed method has been implemented in Matlab environment and provides visualization tools for diagnostic purposes. The obtained results have proven that our algorithm performs well in effectively extracting vehicles from LiDAR data that can contribute to the complex task of traffic flow information evaluation
关键词：LIDAR processing; Object extraction; Algorithm comparison; Point cloud segmentation
