The Robust Semantic SLAM System for Texture-Less Underground Parking Lot
Automatic valet parking (AVP) is the autonomous driving function that may take the lead in mass production. AVP is usually needed in an underground parking lot, where the light is dim, the parking space is narrow, and the GPS signal is denied. The traditional visual-based simultaneous location and m...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/9681455 |
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| _version_ | 1832549038642167808 |
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| author | Chongjun Liu Jianjun Yao |
| author_facet | Chongjun Liu Jianjun Yao |
| author_sort | Chongjun Liu |
| collection | DOAJ |
| description | Automatic valet parking (AVP) is the autonomous driving function that may take the lead in mass production. AVP is usually needed in an underground parking lot, where the light is dim, the parking space is narrow, and the GPS signal is denied. The traditional visual-based simultaneous location and mapping (SLAM) algorithm suffers from localization loss because of inaccurate mapping results. A new robust semantic SLAM system is designed mainly for the dynamic low-texture underground parking lot to solve the problem mentioned. In this system, a 16-channel Lidar is used to help the visual system build an accurate semantic map. Four fisheye cameras mounted at the front, back, left, and right of the vehicle are also used to produce the bird’s eye view picture of the vehicle by joint calibration. The vehicle can localize itself and navigate to the target parking lot with the semantic segmented picture and the preobtained semantic map. Based on the experiment result, the proposed AVP-SLAM solution is robust in the underground parking lot. |
| format | Article |
| id | doaj-art-737d6a6280a948cb8812529c8f21e98d |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-737d6a6280a948cb8812529c8f21e98d2025-02-03T06:12:24ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/9681455The Robust Semantic SLAM System for Texture-Less Underground Parking LotChongjun Liu0Jianjun Yao1College of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringAutomatic valet parking (AVP) is the autonomous driving function that may take the lead in mass production. AVP is usually needed in an underground parking lot, where the light is dim, the parking space is narrow, and the GPS signal is denied. The traditional visual-based simultaneous location and mapping (SLAM) algorithm suffers from localization loss because of inaccurate mapping results. A new robust semantic SLAM system is designed mainly for the dynamic low-texture underground parking lot to solve the problem mentioned. In this system, a 16-channel Lidar is used to help the visual system build an accurate semantic map. Four fisheye cameras mounted at the front, back, left, and right of the vehicle are also used to produce the bird’s eye view picture of the vehicle by joint calibration. The vehicle can localize itself and navigate to the target parking lot with the semantic segmented picture and the preobtained semantic map. Based on the experiment result, the proposed AVP-SLAM solution is robust in the underground parking lot.http://dx.doi.org/10.1155/2022/9681455 |
| spellingShingle | Chongjun Liu Jianjun Yao The Robust Semantic SLAM System for Texture-Less Underground Parking Lot Journal of Advanced Transportation |
| title | The Robust Semantic SLAM System for Texture-Less Underground Parking Lot |
| title_full | The Robust Semantic SLAM System for Texture-Less Underground Parking Lot |
| title_fullStr | The Robust Semantic SLAM System for Texture-Less Underground Parking Lot |
| title_full_unstemmed | The Robust Semantic SLAM System for Texture-Less Underground Parking Lot |
| title_short | The Robust Semantic SLAM System for Texture-Less Underground Parking Lot |
| title_sort | robust semantic slam system for texture less underground parking lot |
| url | http://dx.doi.org/10.1155/2022/9681455 |
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