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：2008
卷号：XXXVII Part B3b
页码：515-522
出版社：Copernicus Publications
摘要：Land-based mobile mapping systems have yielded an enormous time saving in capturing road networks and their surrounding. However, the manual extraction of the road information from the mobile mapping data is still a time-consuming task. This paper presents ARVEE (Automated Road Geometry Vectors Extraction Engine), a robust automatic road geometry extraction system developed by Absolute Mapping Solution Inc. (AMS). The extracted road information includes 3D continuous lane lines, road edges as well as lane lines attributes. There are three innovations in this work. First, all the visible lane lines in the georeferenced image sequences are extracted, instead of only extracting the central lane line or the nearby lane line pair. Second, lane line attributes are recognized, so the output is a functional description of the road geometry. Third, the output is an absolute-georeferenced model of lane lines in mapping coordinates, and is directly compatible to GIS databases. ARVEE includes four steps: First, extracting linear features in each image. Second, extracting, filtering and grouping linear features into lane line segments (LLS) based on their geometric and radiometric characteristics. Third, linking the LLSs into long lane lines 3D model using Multiple-Hypothesis Analysis (MHA). Finally, classifying each lane line into a lane line type based on the synthetic analysis of the included LLSs' features. The system has been tested on large number of VISAT. mobile mapping data. The experiments on massive real MMS data sets demonstrate that ARVEE can deliver accurate and robust 3D continuous functional road geometry model. Full automatic processing result from ARVEE can replace most of the human efforts in road geometry modelling
