Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images
Automatic charging for electric vehicles has broad development prospects for meeting the personalized service experience of users while overcoming the inherent safety hazards. An identification and positioning approach suitable for engineering applications is the key to promoting automatic charging....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/5247 |
| _version_ | 1797501745998331904 |
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| author | Taoyong Li Chunlei Xia Ming Yu Panpan Tang Wei Wei Dongmei Zhang |
| author_facet | Taoyong Li Chunlei Xia Ming Yu Panpan Tang Wei Wei Dongmei Zhang |
| author_sort | Taoyong Li |
| collection | DOAJ |
| description | Automatic charging for electric vehicles has broad development prospects for meeting the personalized service experience of users while overcoming the inherent safety hazards. An identification and positioning approach suitable for engineering applications is the key to promoting automatic charging. In this paper, a low-cost, high-precision method to identify and position charging ports based on SIFT and SGBM is proposed. The feature extraction approach based on SIFT is adopted to produce the difference of Gaussian (DOG) for scale space construction, and the feature matching algorithm with nearest-neighbor search, which is a kind of machine learning, is utilized to yield the map set of matching points. In addition, the disparity calculation is conducted with a semi-global matching algorithm to obtain high-precision positioning results for the charging port. In order to verify the feasibility of the method, a complete identification and positioning experiment of charging port was carried out based on OpenCV and MATLAB. |
| first_indexed | 2024-03-10T03:22:59Z |
| format | Article |
| id | doaj.art-fa989804026a45c1bb5938e5b2c01eb2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:22:59Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fa989804026a45c1bb5938e5b2c01eb22023-11-23T09:59:51ZengMDPI AGApplied Sciences2076-34172022-05-011210524710.3390/app12105247Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular ImagesTaoyong Li0Chunlei Xia1Ming Yu2Panpan Tang3Wei Wei4Dongmei Zhang5Beijing Engineering Technology Research Center of Electric Vehicle Charging/Battery Swap, Beijing 100194, ChinaBeijing Yupont Electric Power Technology Co., Ltd., Beijing 100029, ChinaSchool of Engineering, Beijing Forestry University, Beijing 100083, ChinaBeijing Engineering Technology Research Center of Electric Vehicle Charging/Battery Swap, Beijing 100194, ChinaBeijing Yupont Electric Power Technology Co., Ltd., Beijing 100029, ChinaBeijing Engineering Technology Research Center of Electric Vehicle Charging/Battery Swap, Beijing 100194, ChinaAutomatic charging for electric vehicles has broad development prospects for meeting the personalized service experience of users while overcoming the inherent safety hazards. An identification and positioning approach suitable for engineering applications is the key to promoting automatic charging. In this paper, a low-cost, high-precision method to identify and position charging ports based on SIFT and SGBM is proposed. The feature extraction approach based on SIFT is adopted to produce the difference of Gaussian (DOG) for scale space construction, and the feature matching algorithm with nearest-neighbor search, which is a kind of machine learning, is utilized to yield the map set of matching points. In addition, the disparity calculation is conducted with a semi-global matching algorithm to obtain high-precision positioning results for the charging port. In order to verify the feasibility of the method, a complete identification and positioning experiment of charging port was carried out based on OpenCV and MATLAB.https://www.mdpi.com/2076-3417/12/10/5247identification and location of charging portSIFT feature extractionnearest neighbor search feature matchingsemi-global matching methoddisparity calculation |
| spellingShingle | Taoyong Li Chunlei Xia Ming Yu Panpan Tang Wei Wei Dongmei Zhang Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images Applied Sciences identification and location of charging port SIFT feature extraction nearest neighbor search feature matching semi-global matching method disparity calculation |
| title | Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images |
| title_full | Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images |
| title_fullStr | Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images |
| title_full_unstemmed | Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images |
| title_short | Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images |
| title_sort | scale invariant localization of electric vehicle charging port via semi global matching of binocular images |
| topic | identification and location of charging port SIFT feature extraction nearest neighbor search feature matching semi-global matching method disparity calculation |
| url | https://www.mdpi.com/2076-3417/12/10/5247 |
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