Real-Time Position and Attitude Estimation for Homing and Docking of an Autonomous Underwater Vehicle Based on Bionic Polarized Optical Guidance

Cheng, Haoyuan; Chu, Jinkui; Zhang, Ran; Gui, Xinyuan; Tian, Lei

doi:10.1007/s11802-020-4399-z

Cited by 19 publications

(8 citation statements)

References 21 publications

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“…The underwater robot estimates its own pose parameters through the detection of polarized light sources. H. Y. Cheng et al [141] experimented with a robot pose estimation method based on a polarized light matrix on the docking platform (DS) for autonomous underwater robots. After the experiment, they obtained results with an error of within 0.116 m within a distance of 100 m.…”

Section: Other Localization Methods In Confined Spacesmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

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Section: Other Localization Methods In Confined Spacesmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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“…This approach performs well in pool tests, surpassing traditional methods like OI and EPNP. Dalian University of Technology [43] proposed a polarization-based optical guidance method utilizing four polarized artificial underwater landmarks. This approach leverages the long-distance propagation of polarized light underwater, offering advantages in communication and navigation.…”

Section: Traditional Feature Extraction Algorithmsmentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. Underwater vision navigation systems serve as a pivotal technology, encompassing various domains such as computer vision, image processing, and machine learning. Nevertheless, they confront a series of challenges. This review aims to consolidate the most recent research advancements in underwater vision navigation systems, with a particular focus on critical aspects such as beacon design, monocular and binocular vision, and detection algorithms. At present, underwater beacons can be categorized into active and passive types, with the flexibility to choose between them contingent upon the specific task and environment. On the other hand, relative to the prevalent use of monocular vision, binocular vision systems offer the potential to furnish more accurate depth information, and the seamless integration of monocular and binocular vision systems enhances the precision and stability of navigation tasks. Concerning detection algorithms, traditional feature extraction methods and deep learning approaches each exhibit their merits and drawbacks, but deep learning methods demonstrate immense potential in complex underwater environments. By summarizing and analyzing the above research results, the application of each key technology of underwater vision navigation and docking is sorted out, and its further development direction is foreseen.

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“…The marine ecosystem is one of the most productive and dynamic ecosystems on earth. Many scholars have conducted research in marine resource exploration, biological investigation, underwater vehicle navigation, and other fields [ 1 , 2 , 3 , 4 ]. Underwater optical images are one of the important media for exploring the ocean at present.…”

Section: Introductionmentioning

confidence: 99%

Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism

Cheng

Zhang

Zhu

et al. 2023

Sensors

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Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion network is applied to underwater polarization images; that is, the underwater polarization images are fused with intensity images using the deep learning method. To construct a training dataset, we establish an experimental setup to obtain underwater polarization images and perform appropriate transformations to expand the dataset. Next, an end-to-end learning framework based on unsupervised learning and guided by an attention mechanism is constructed for fusing polarization and light intensity images. The loss function and weight parameters are elaborated. The produced dataset is used to train the network under different loss weight parameters, and the fused images are evaluated based on different image evaluation metrics. The results show that the fused underwater images are more detailed. Compared with light intensity images, the information entropy and standard deviation of the proposed method increase by 24.48% and 139%. The image processing results are better than other fusion-based methods. In addition, the improved U-net network structure is used to extract features for image segmentation. The results show that the target segmentation based on the proposed method is feasible under turbid water. The proposed method does not require manual adjustment of weight parameters, has faster operation speed, and has strong robustness and self-adaptability, which is important for research in vision fields, such as ocean detection and underwater target recognition.

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Real-Time Position and Attitude Estimation for Homing and Docking of an Autonomous Underwater Vehicle Based on Bionic Polarized Optical Guidance

Cited by 19 publications

References 21 publications

A Review of Underwater Robot Localization in Confined Spaces

A Review of Underwater Robot Localization in Confined Spaces

Review of underwater visual navigation and docking: advances and challenges

Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism

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