Machine vision based perception for vehicle-mounted UAV autonomous landing under GNSS-denied environments
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| Udgivet i: | Journal of King Saud University. Computer and Information Sciences vol. 37, no. 10 (Dec 2025), p. 334 |
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| Andre forfattere: | , , , , , , , |
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Springer Nature B.V.
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| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3275907296 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1319-1578 | ||
| 024 | 7 | |a 10.1007/s44443-025-00345-3 |2 doi | |
| 035 | |a 3275907296 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Ma, Pengbo |u Jiangsu University, School of Automotive and Traffic Engineering, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X) | |
| 245 | 1 | |a Machine vision based perception for vehicle-mounted UAV autonomous landing under GNSS-denied environments | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the growing demand for collaborative Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) operations, precise landing of a vehicle-mounted UAV on a moving platform in complex environments has become a significant challenge, limiting the functionality of collaborative systems. This paper presents an autonomous landing perception scheme for a vehicle-mounted UAV, specifically designed for GNSS-denied environments to enhance landing capabilities. First, to address the challenges of insufficient illumination in airborne visual perception, an airborne infrared and visible image fusion method is employed to enhance image detail and contrast. Second, a feature enhancement network and region proposal network optimized for small object detection are explored to improve the detection of moving platforms during UAV landing. Finally, a relative pose and position estimation method based on the orthogonal iteration algorithm is investigated to reduce visual pose and position estimation errors and iteration time. Both simulation results and field tests demonstrate that the proposed algorithm performs robustly under low-light and foggy conditions, achieving accurate pose and position estimation even in scenarios with inadequate illumination. | |
| 653 | |a Field tests | ||
| 653 | |a Landing | ||
| 653 | |a Iterative algorithms | ||
| 653 | |a Usability | ||
| 653 | |a Deep learning | ||
| 653 | |a Collaboration | ||
| 653 | |a Machine vision | ||
| 653 | |a Visual perception | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Infrared imagery | ||
| 653 | |a Unmanned ground vehicles | ||
| 653 | |a Computer vision | ||
| 653 | |a Algorithms | ||
| 653 | |a Object recognition | ||
| 653 | |a Light | ||
| 653 | |a Illumination | ||
| 653 | |a Border patrol | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Vehicles | ||
| 700 | 1 | |a He, Chenyuan |u Jiangsu University, School of Automotive and Traffic Engineering, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X); the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing, China (GRID:grid.440785.a); Control and Safety Key Laboratory of Sichuan Province, Vehicle Measurement, Chengdu, China (GRID:grid.440785.a) | |
| 700 | 1 | |a Zhang, Zhouyu |u Jiangsu University, School of Automotive and Traffic Engineering, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X) | |
| 700 | 1 | |a Xv, Zhan |u Jiangsu University, School of Automotive and Traffic Engineering, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X) | |
| 700 | 1 | |a Wang, Hai |u Jiangsu University, School of Automotive and Traffic Engineering, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X) | |
| 700 | 1 | |a Cai, Yingfeng |u Jiangsu University, Automotive Engineering Research Institute, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X) | |
| 700 | 1 | |a Chen, Long |u Jiangsu University, Automotive Engineering Research Institute, Zhenjiang, China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X) | |
| 700 | 1 | |a Zhong, Can |u Beijing Engineering Research Center of Aerial Intelligent Remote Sensing Equipments, Beijing, China (GRID:grid.440785.a) | |
| 700 | 1 | |a Zhang, Yiqun |u TopXGun (Nanjing) Robotics Company Limited, Nanjing, China (GRID:grid.440785.a) | |
| 773 | 0 | |t Journal of King Saud University. Computer and Information Sciences |g vol. 37, no. 10 (Dec 2025), p. 334 | |
| 786 | 0 | |d ProQuest |t Computer Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275907296/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3275907296/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275907296/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |