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Automatic Parking Based on a Bird's Eye View Vision System
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- Chunxiang Wang
- Research Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
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- Hengrun Zhang
- Department of Automation, Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
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- Ming Yang
- Department of Automation, Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
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- Xudong Wang
- Research Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
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- Lei Ye
- College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China
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- Chunzhao Guo
- Toyota Central R&D Labs., Inc., Nagakute 4801192, Japan
Description
<jats:p> This paper aims at realizing an automatic parking method through a bird's eye view vision system. With this method, vehicles can make robust and real-time detection and recognition of parking spaces. During parking process, the omnidirectional information of the environment can be obtained by using four on-board fisheye cameras around the vehicle, which are the main part of the bird's eye view vision system. In order to achieve this purpose, a polynomial fisheye distortion model is firstly used for camera calibration. An image mosaicking method based on the Levenberg-Marquardt algorithm is used to combine four individual images from fisheye cameras into one omnidirectional bird's eye view image. Secondly, features of the parking spaces are extracted with a Radon transform based method. Finally, double circular trajectory planning and a preview control strategy are utilized to realize autonomous parking. Through experimental analysis, we can see that the proposed method can get effective and robust real-time results in both parking space recognition and automatic parking. </jats:p>
Journal
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- Advances in Mechanical Engineering
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Advances in Mechanical Engineering 6 847406-, 2014-01-01
SAGE Publications
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Details 詳細情報について
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- CRID
- 1361699995197672960
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- ISSN
- 16878140
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- Data Source
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- Crossref