A Review on Deep Learning for UAV Absolute Visual Localization
In the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This s...
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MDPI AG
2024-10-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/622 |
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| author | Andy Couturier Moulay A. Akhloufi |
| author_facet | Andy Couturier Moulay A. Akhloufi |
| author_sort | Andy Couturier |
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| description | In the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This surge in adoption can be attributed to the UAV ecosystem’s maturation, which has not only made these devices more accessible and cost effective but has also significantly enhanced their operational capabilities in terms of flight duration and embedded computing power. In conjunction with these developments, the research on Absolute Visual Localization (AVL) has seen a resurgence driven by the introduction of deep learning to the field. These new approaches have significantly improved localization solutions in comparison to the previous generation of approaches based on traditional computer vision feature extractors. This paper conducts an extensive review of the literature on deep learning-based methods for UAV AVL, covering significant advancements since 2019. It retraces key developments that have led to the rise in learning-based approaches and provides an in-depth analysis of related localization sources such as Inertial Measurement Units (IMUs) and Global Navigation Satellite Systems (GNSSs), highlighting their limitations and advantages for more effective integration with AVL. The paper concludes with an analysis of current challenges and proposes future research directions to guide further work in the field. |
| format | Article |
| id | doaj-art-bb0c81aae6b749f4b1ba2ce12255bd87 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-bb0c81aae6b749f4b1ba2ce12255bd872025-08-20T02:08:03ZengMDPI AGDrones2504-446X2024-10-0181162210.3390/drones8110622A Review on Deep Learning for UAV Absolute Visual LocalizationAndy Couturier0Moulay A. Akhloufi1Computer Science Department, Science Faculty, Université de Moncton, 18 Antonine-Maillet Ave, Moncton, NB E1A 3E9, CanadaComputer Science Department, Science Faculty, Université de Moncton, 18 Antonine-Maillet Ave, Moncton, NB E1A 3E9, CanadaIn the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This surge in adoption can be attributed to the UAV ecosystem’s maturation, which has not only made these devices more accessible and cost effective but has also significantly enhanced their operational capabilities in terms of flight duration and embedded computing power. In conjunction with these developments, the research on Absolute Visual Localization (AVL) has seen a resurgence driven by the introduction of deep learning to the field. These new approaches have significantly improved localization solutions in comparison to the previous generation of approaches based on traditional computer vision feature extractors. This paper conducts an extensive review of the literature on deep learning-based methods for UAV AVL, covering significant advancements since 2019. It retraces key developments that have led to the rise in learning-based approaches and provides an in-depth analysis of related localization sources such as Inertial Measurement Units (IMUs) and Global Navigation Satellite Systems (GNSSs), highlighting their limitations and advantages for more effective integration with AVL. The paper concludes with an analysis of current challenges and proposes future research directions to guide further work in the field.https://www.mdpi.com/2504-446X/8/11/622unmanned aerial vehiclesdeep learninglocalizationGNSS-denied navigation |
| spellingShingle | Andy Couturier Moulay A. Akhloufi A Review on Deep Learning for UAV Absolute Visual Localization Drones unmanned aerial vehicles deep learning localization GNSS-denied navigation |
| title | A Review on Deep Learning for UAV Absolute Visual Localization |
| title_full | A Review on Deep Learning for UAV Absolute Visual Localization |
| title_fullStr | A Review on Deep Learning for UAV Absolute Visual Localization |
| title_full_unstemmed | A Review on Deep Learning for UAV Absolute Visual Localization |
| title_short | A Review on Deep Learning for UAV Absolute Visual Localization |
| title_sort | review on deep learning for uav absolute visual localization |
| topic | unmanned aerial vehicles deep learning localization GNSS-denied navigation |
| url | https://www.mdpi.com/2504-446X/8/11/622 |
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