UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy
The low-altitude economy heavily relies on new carriers represented by unmanned aerial vehicles (UAVs). The localization accuracy of UAVs highly relies on the Global Navigation Satellite System (GNSS), which can be easily affected in low-altitude urban environments, making it difficult to maintain e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/14 |
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| author | Yineng Li Qinghua Zeng Chen Shao Peng Zhuo Bowen Li Kecheng Sun |
| author_facet | Yineng Li Qinghua Zeng Chen Shao Peng Zhuo Bowen Li Kecheng Sun |
| author_sort | Yineng Li |
| collection | DOAJ |
| description | The low-altitude economy heavily relies on new carriers represented by unmanned aerial vehicles (UAVs). The localization accuracy of UAVs highly relies on the Global Navigation Satellite System (GNSS), which can be easily affected in low-altitude urban environments, making it difficult to maintain effective localization accuracy. To solve this problem, this paper proposes a UAV autonomous localization method with keypoints on the edges of semantic objects (KESO). Firstly, semantic objects within the working area are selected, and then the latitude, longitude, and altitude of these semantic objects’ keypoints are measured to construct a database. By identifying the semantic objects from aerial images and detecting the edge of the semantic objects, the keypoints of the semantic objects are obtained. Finally, by matching the detected keypoints in the aerial images with the keypoints in the database, the UAV’s position can achieve a high-precision position when satellite signals are blocked in low-altitude urban environments. As verified by real flight data, the results show that the localization error is less than 5 m, and the edges of objects can obtain more accurate keypoints to help UAVs locate more accurately. This paper can provide a reference for UAV localization in the urban environments of the low-altitude economy. |
| format | Article |
| id | doaj-art-e2d3206269524cea9fc0f9a8c2699145 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e2d3206269524cea9fc0f9a8c26991452025-01-24T13:29:38ZengMDPI AGDrones2504-446X2024-12-01911410.3390/drones9010014UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude EconomyYineng Li0Qinghua Zeng1Chen Shao2Peng Zhuo3Bowen Li4Kecheng Sun5Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, ChinaNavigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, ChinaNavigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, ChinaNavigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, ChinaNavigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaThe low-altitude economy heavily relies on new carriers represented by unmanned aerial vehicles (UAVs). The localization accuracy of UAVs highly relies on the Global Navigation Satellite System (GNSS), which can be easily affected in low-altitude urban environments, making it difficult to maintain effective localization accuracy. To solve this problem, this paper proposes a UAV autonomous localization method with keypoints on the edges of semantic objects (KESO). Firstly, semantic objects within the working area are selected, and then the latitude, longitude, and altitude of these semantic objects’ keypoints are measured to construct a database. By identifying the semantic objects from aerial images and detecting the edge of the semantic objects, the keypoints of the semantic objects are obtained. Finally, by matching the detected keypoints in the aerial images with the keypoints in the database, the UAV’s position can achieve a high-precision position when satellite signals are blocked in low-altitude urban environments. As verified by real flight data, the results show that the localization error is less than 5 m, and the edges of objects can obtain more accurate keypoints to help UAVs locate more accurately. This paper can provide a reference for UAV localization in the urban environments of the low-altitude economy.https://www.mdpi.com/2504-446X/9/1/14unmanned aerial vehiclelow-altitude urban environmentssemantic objectskeypoints on the edgeslow-altitude economy |
| spellingShingle | Yineng Li Qinghua Zeng Chen Shao Peng Zhuo Bowen Li Kecheng Sun UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy Drones unmanned aerial vehicle low-altitude urban environments semantic objects keypoints on the edges low-altitude economy |
| title | UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy |
| title_full | UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy |
| title_fullStr | UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy |
| title_full_unstemmed | UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy |
| title_short | UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy |
| title_sort | uav localization method with keypoints on the edges of semantic objects for low altitude economy |
| topic | unmanned aerial vehicle low-altitude urban environments semantic objects keypoints on the edges low-altitude economy |
| url | https://www.mdpi.com/2504-446X/9/1/14 |
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