Low-cost Underwater Localisation Using Single-Beam Echosounders and Inertial Measurement Units

Azam, Abu Bakr; Jie Kong, Ze; Ng, Sing Yew; Scherrer Florian, Michael; Elhadidi, Basman; Seet, Gerald; Zheng, Jianmin; Cai, Yiyu

doi:10.1109/oceanslimerick52467.2023.10244255

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[16] Single-beam Sonar A single-beam scanning sonar for imaging in low-visibility conditions offers distance information over several meters and is immune to water turbidity. [31,32] Multibeam Sonar…”

Section: Sonar Type Description Reference Active Sonarmentioning

confidence: 99%

“…i) Background Context The Earthʹs oceans cover about 71% of the planetʹs surface, holding immense value for resources, scientific exploration, and environmental understanding [2,3]. Unmanned Underwater Vehicles (UUVs) [4,5] are instrumental in various applications, from marine mining to pipeline inspection, but their effectiveness is hindered by the limitations of traditional navigation methods [6,7]. These methods, such as inertial sensors and acoustic beacons, struggle in challenging underwater conditions due to cumulative errors, limited range, and environmental interference [7,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Merveille,

Jia,

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Sonar Type Description Reference Active Sonarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Merveille,

Jia,

2024

Preprint

View full text Add to dashboard Cite

show abstract

Acoustic Positioning Enhanced Optical Image Mosaics: A Collaborative Robot-Assisted Approach

Azam,

Kien,

Loke

et al. 2024

OCEANS 2024 - Singapore

View full text Add to dashboard Cite

Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration

Merveille,

Jia,

et al. 2024

Sensors

View full text Add to dashboard Cite

Underwater simultaneous localization and mapping (SLAM) is essential for effectively navigating and mapping underwater environments; however, traditional SLAM systems have limitations due to restricted vision and the constantly changing conditions of the underwater environment. This study thoroughly examined the underwater SLAM technology, particularly emphasizing the incorporation of deep learning methods to improve performance. We analyzed the advancements made in underwater SLAM algorithms. We explored the principles behind SLAM and deep learning techniques, examining how these methods tackle the specific difficulties encountered in underwater environments. The main contributions of this work are a thorough assessment of the research into the use of deep learning in underwater image processing and perception and a comparison study of standard and deep learning-based SLAM systems. This paper emphasizes specific deep learning techniques, including generative adversarial networks (GANs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and other advanced methods to enhance feature extraction, data fusion, scene understanding, etc. This study highlights the potential of deep learning in overcoming the constraints of traditional underwater SLAM methods, providing fresh opportunities for exploration and industrial use.

show abstract

Low-cost Underwater Localisation Using Single-Beam Echosounders and Inertial Measurement Units

Cited by 3 publications

References 14 publications

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Acoustic Positioning Enhanced Optical Image Mosaics: A Collaborative Robot-Assisted Approach

Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration

Contact Info

Product

Resources

About