Version 1
: Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (11:36:27 CEST)
How to cite:
Merveille, F.F.R.; Jia, B.; Xu, Z. Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles. Preprints2024, 2024040548. https://doi.org/10.20944/preprints202404.0548.v1
Merveille, F.F.R.; Jia, B.; Xu, Z. Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles. Preprints 2024, 2024040548. https://doi.org/10.20944/preprints202404.0548.v1
Merveille, F.F.R.; Jia, B.; Xu, Z. Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles. Preprints2024, 2024040548. https://doi.org/10.20944/preprints202404.0548.v1
APA Style
Merveille, F.F.R., Jia, B., & Xu, Z. (2024). Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles. Preprints. https://doi.org/10.20944/preprints202404.0548.v1
Chicago/Turabian Style
Merveille, F.F.R., Baozhu Jia and Zhizun Xu. 2024 "Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles" Preprints. https://doi.org/10.20944/preprints202404.0548.v1
Abstract
Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment.
Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.
Keywords
Visual Simultaneous Localization and Mapping (SLAM), Unmanned Underwater Vehicle, Deep learning, Underwater Navigation, Sensor integration
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
