A survey of sensors based autonomous unmanned aerial vehicle (UAV) localization techniques
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| Publicado en: | Complex & Intelligent Systems vol. 11, no. 8 (Aug 2025), p. 371 |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s40747-025-01961-2 |2 doi | |
| 035 | |a 3226844689 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 100 | 1 | |a Liu, Haiqiao |u Hunan Institute of Engineering, School of Electrical and Information Engineering, Hunan, China (GRID:grid.459468.2) (ISNI:0000 0004 1793 4133) | |
| 245 | 1 | |a A survey of sensors based autonomous unmanned aerial vehicle (UAV) localization techniques | |
| 260 | |b Springer Nature B.V. |c Aug 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Autonomous localization methods for Unmanned Aerial Vehicles (UAVs) have significant application potential in complex environments. This paper presents a comprehensive survey of UAV localization techniques, focusing on both pure vision-based and sensor-assisted approaches. For pure vision-based localization, the survey emphasizes key technologies for feature descriptor generation, advancements in similarity measurement criteria, and optimized computational strategies. The impact of these technologies on improving computational efficiency and localization accuracy. In the context of sensor-assisted multi-source UAV localization, the applications of filtering-based fusion, optimization-based fusion, and deep learning-based fusion methods are discussed. A detailed analysis demonstrates the advantages of multi-modal data fusion in improving robustness and accuracy. Despite significant progress in localization accuracy and adaptability to complex environments, challenges remain in adapting to low-texture environments, optimizing fusion strategies, and addressing computational resource limitations. Finally, the paper discusses future directions for the research and implementation of UAV autonomous localization methods. | |
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Magnetic fields | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Data integration | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Modal data | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Localization | ||
| 700 | 1 | |a Long, Qing |u Graduate School of Hunan University of Engineering, Hunan, China (GRID:grid.67293.39) | |
| 700 | 1 | |a Yi, Bing |u Hunan Institute of Engineering, School of Materials and Chemical Engineering, Hunan, China (GRID:grid.459468.2) (ISNI:0000 0004 1793 4133) | |
| 700 | 1 | |a Jiang, Wen |u Graduate School of Hunan University of Engineering, Hunan, China (GRID:grid.67293.39) | |
| 773 | 0 | |t Complex & Intelligent Systems |g vol. 11, no. 8 (Aug 2025), p. 371 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3226844689/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3226844689/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3226844689/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |