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A survey of sensors based autonomous unmanned aerial vehicle (UAV) localization techniques

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Pubblicato in:Complex & Intelligent Systems vol. 11, no. 8 (Aug 2025), p. 371
Autore principale: Liu, Haiqiao
Altri autori: Long, Qing, Yi, Bing, Jiang, Wen
Pubblicazione:
Springer Nature B.V.
Soggetti:
Global positioning systems > GPS
Accuracy
Deep learning
Unmanned aerial vehicles
Magnetic fields
Sensors
Neural networks
Optimization
Data integration
Methods
Algorithms
Modal data
Research & development > R&D
Localization
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Abstract: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.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-025-01961-2
Fonte:Advanced Technologies & Aerospace Database