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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 713753, 14 pages
Research Article

Panorama-Based Multilane Recognition for Advanced Navigation Map Generation

1Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
2Research Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
3College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China

Received 30 July 2014; Accepted 12 September 2014

Academic Editor: Wuhong Wang

Copyright © 2015 Ming Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Precise navigation map is crucial in many fields. This paper proposes a panorama based method to detect and recognize lane markings and traffic signs on the road surface. Firstly, to deal with the limited field of view and the occlusion problem, this paper designs a vision-based sensing system which consists of a surround view system and a panoramic system. Secondly, in order to detect and identify traffic signs on the road surface, sliding window based detection method is proposed. Template matching method and SVM (Support Vector Machine) are used to recognize the traffic signs. Thirdly, to avoid the occlusion problem, this paper utilities vision based ego-motion estimation to detect and remove other vehicles. As surround view images contain less dynamic information and gray scales, improved ICP (Iterative Closest Point) algorithm is introduced to ensure that the ego-motion parameters are consequently obtained. For panoramic images, optical flow algorithm is used. The results from the surround view system help to filter the optical flow and optimize the ego-motion parameters; other vehicles are detected by the optical flow feature. Experimental results show that it can handle different kinds of lane markings and traffic signs well.