期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 20, 期 7, 页码 2550-2565出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2018.2868168
关键词
Point cloud; multi-view images; mobile laser scanning (MLS); traffic sign; joint 2-D-3-D; geo-localization
资金
- National Science Foundation of China [61771413, U1605254]
- Fundamental Research Funds for the Central Universities [20720170047]
This paper presents a framework to build a joint 2-D-3-D traffic sign landmark data set for geo-localization using mobile laser scanning (MLS) data. The MLS data include 3-D point clouds and corresponding multi-view images. First, an integrated method, based on a deep learning network and the retro-reflective properties of traffic signs, is developed to accurately extract traffic signs from MLS point clouds. Next, the semantic and spatial properties of the traffic signs (type, location, position, and geometric characteristics) are obtained. Then, a joint 2-D-3-D traffic sign landmark data set is built, and a semantic-spatial organization graph is used to organize the traffic sign data set. Last, based on the traffic sign landmark data set, a geo-localization method for a driving car is proposed to estimate the driving trajectory. It can be used for auxiliary positioning of autonomous vehicles. Experimental results demonstrate the reliability of our proposed method for traffic sign detection and the potential of building 2-D-3-D traffic sign landmark data set for driving trajectory estimation from MLS data.
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