AUTOMATED 3D ROAD SIGN MAPPING WITH STEREOVISION-BASED MOBILE MAPPING EXPLOITING DISPARITY INFORMATION FROM DENSE STEREO MATCHING
- Institute of Geomatics Engineering, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
Keywords: Mobile, Mapping, Point Cloud, Extraction, Classification, Matching, Infrastructure, Inventory
Abstract. This paper presents algorithms and investigations on the automated detection, classification and mapping of road signs which systematically exploit depth information from stereo images. This approach was chosen due to recent progress in the development of stereo matching algorithms enabling the generation of accurate and dense depth maps. In comparison to mono imagery-based approaches, depth maps also allow 3D mapping of the objects. This is essential for efficient inventory and for future change detection purposes. Test measurements with the mobile mapping system by the Institute of Geomatics Engineering of the FHNW University of Applied Sciences and Arts Northwestern Switzerland demonstrated that the developed algorithms for the automated 3D road sign mapping perform well, even under difficult to poor lighting conditions. Approximately 90% of the relevant road signs with predominantly red, blue and yellow colors in Switzerland can be detected, and 85% can be classified correctly. Furthermore, fully automated mapping with a 3D accuracy of better than 10 cm is possible.