Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing
Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs ba...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/21/8385 |
| _version_ | 1797466436289953792 |
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| author | Ning Ma Xiangrui Weng Yunfeng Cao Linbin Wu |
| author_facet | Ning Ma Xiangrui Weng Yunfeng Cao Linbin Wu |
| author_sort | Ning Ma |
| collection | DOAJ |
| description | Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings. |
| first_indexed | 2024-03-09T18:39:51Z |
| format | Article |
| id | doaj.art-21f689fc21f44673a7f89ae8b227fe76 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:39:51Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-21f689fc21f44673a7f89ae8b227fe762023-11-24T06:47:15ZengMDPI AGSensors1424-82202022-11-012221838510.3390/s22218385Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV LandingNing Ma0Xiangrui Weng1Yunfeng Cao2Linbin Wu3College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaImproving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings.https://www.mdpi.com/1424-8220/22/21/8385UAVsstate estimationmonocular visionrunway detection |
| spellingShingle | Ning Ma Xiangrui Weng Yunfeng Cao Linbin Wu Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing Sensors UAVs state estimation monocular vision runway detection |
| title | Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing |
| title_full | Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing |
| title_fullStr | Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing |
| title_full_unstemmed | Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing |
| title_short | Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing |
| title_sort | monocular vision based precise runway detection applied to state estimation for carrier based uav landing |
| topic | UAVs state estimation monocular vision runway detection |
| url | https://www.mdpi.com/1424-8220/22/21/8385 |
| work_keys_str_mv | AT ningma monocularvisionbasedpreciserunwaydetectionappliedtostateestimationforcarrierbaseduavlanding AT xiangruiweng monocularvisionbasedpreciserunwaydetectionappliedtostateestimationforcarrierbaseduavlanding AT yunfengcao monocularvisionbasedpreciserunwaydetectionappliedtostateestimationforcarrierbaseduavlanding AT linbinwu monocularvisionbasedpreciserunwaydetectionappliedtostateestimationforcarrierbaseduavlanding |