GENERATION ON SEMANTIC POINT CLOUD DATA OF ROAD FEATURES USING DYNAMIC MAPS

Abstract

 In recent construction work sites, a large amount of point cloud data for various places have been measured and accumulated along with the technological innovation of survey instruments such as mobile mapping systems (MMS) and ground-based laser scanners and cameras. The point cloud data are used mainly for preparing maps and recognizing the shapes of structures. Nevertheless, since the point cloud data are an aggregation of massive points, if the semantic information of road features, such as dividing lines and traffic signs, can be provided with the data and recognized by the computer, increased efficiency and advancement of work as well as versatility can be expected.

 In this study, location information of road features, such as dividing lines and traffic signs from a dynamic map, which are high-precision data, is analyzed, and a method of converting it into area data conforming to the “Attribute management specifications for point cloud data [for roads] (draft)” is devised. Then, with a case study conforming to the devised method, it is clarified that the point cloud data of road features can be extracted using the area data.