Diver tracking in open waters: A low‐cost approach based on visual and acoustic sensor fusion

Remmas, Walid; Chemori, Ahmed; Kruusmaa, Maarja

doi:10.1002/rob.21999

Cited by 30 publications

(9 citation statements)

References 54 publications

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“…Given the limited visibility in this simulation, navigation methods using natural landscapes as references were excluded due to the extended distances. The systems searched for features from nearer objects, such as the seabed [13], artificial landscapes [14], or the pipeline. A common approach involved extracting underwater pipelines from images using edge detection algorithms [15] [16] [17] [18].…”

Section: B Related Workmentioning

confidence: 99%

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Sang

Norris

2023

IEEE Sensors J.

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Underwater pipeline inspection is an important topic in off-shore subsea operations. ROVs (Remotely Operated Vehicles) can play an important role in multiple application areas including military, ocean science, aquaculture, shipping, and energy. However, using ROVs for inspection is not cost-effective, and the fixed leak detection sensors mounted along the pipeline have limited precision. Although the cost can be significantly reduced by applying AUVs (Autonomous Underwater Vehicles), the unstable current, low visibility and loss of GPS signal make the navigation of AUVs underwater very challenging. Previous studies have been conducted on coordinate-based, vision-based, and fusion-based navigation algorithms. However, the coordinate-based algorithms suffered from the denial of GPS signals while the vision-based methods typically relied on terrain and landscape knowledge that required collection prior to the mission. As a result of these issues, a navigation system for an AUV (Autonomous Underwater Vehicle) that incorporates vision and sonar sensors is presented in this paper. In a ROS/Gazebo-based simulation environment, the AUV had the ability to find and navigate towards the pipeline and continuously traverse along its length. Additionally, with a chemical concentration sensor mounted on the AUV, the system demonstrated the capability of inspecting the pipeline and reporting the leak point with a resolution of 3 meters along the pipeline.

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Section: B Related Workmentioning

confidence: 99%

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Sang

Norris

2023

IEEE Sensors J.

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“…A multi-sensor fusion approach is also proposed, which collects visual and acoustic signals from low-cost cameras and hydrophones, respectively. That fused data analyzed by machine learning algorithm helps the system more robust and effective [ 145 ].…”

Section: Underwater Inspectionsmentioning

confidence: 99%

Review of Underwater Sensing Technologies and Applications

Sun

Cui

Chen

2021

Sensors

115

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As the ocean development process speeds up, the technical means of ocean exploration are being upgraded. Due to the characteristics of seawater and the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply in the underwater environment directly. Especially for the seabed topography, it is impossible to carry out long-distance and accurate detection via electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have come into use. Equipped by submersibles, those underwater sensors can sense underwater wide-range and accurately. Moreover, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. This paper has made a summary of the ocean sensing technologies applied in some critical underwater scenarios, including geological surveys, navigation and communication, marine environmental parameters, and underwater inspections. In order to contain as many submersible-based sensors as possible, we have to make a trade-off on breadth and depth. In the end, the authors predict the development trend of underwater sensor technology based on the future ocean exploration requirements.

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“…Farahnakian et al used a selective search to create a large number of candidate regions on the RGB image [72] and then used other modal sensor data to refine the selection to detect targets on the sea surface. In addition, for underwater vehicle and its interaction with surface vehicle [73], the fusion of acoustic sonar or even geomagnetic sensor and visible light camera effectively expands the space for three-dimensional awareness of the marine environment [74,75].…”

Section: Multimodal Information Fusionmentioning

confidence: 99%

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Qiao

Liu

et al. 2021

JMSE

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The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

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Diver tracking in open waters: A low‐cost approach based on visual and acoustic sensor fusion

Cited by 30 publications

References 54 publications

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

An Autonomous Underwater Vehicle Simulation With Fuzzy Sensor Fusion for Pipeline Inspection

Review of Underwater Sensing Technologies and Applications

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

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