Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Chen, Wenjie; Rahmati, Mehdi; Sadhu, Vidyasagar; Pompili, Dario

doi:10.1145/3366486.3366523

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…Wenjie Chen et al [152] offered a technique to obtain quality photos of underwater localization since previously-reported algorithms, such as simultaneous localization and mapping (SLAM), lacked feature-based extraction qualities, resulting in fuzzy images. To address this issue, a novel technology called visual SLAM employs generative adversarial networks to improve picture quality using assessment criteria.…”

Section: Underwater Vehiclesmentioning

confidence: 99%

A Survey of Trajectory Planning Techniques for Autonomous Systems

et al. 2022

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This work offers an overview of the effective communication techniques for space exploration of ground, aerial, and underwater vehicles. We not only comprehensively summarize the trajectory planning, space exploration, optimization, and other challenges encountered but also present the possible directions for future work. Because a detailed study like this is uncommon in the literature, an attempt has been made to fill the gap for readers interested in path planning. This paper also includes optimization strategies that can be used to implement terrestrial, underwater, and airborne applications. This study addresses numerical, bio-inspired, and hybrid methodologies for each dimension described. Throughout this study, we endeavored to establish a centralized platform in which a wealth of research on autonomous vehicles (on the land and their trajectory optimizations), airborne vehicles, and underwater vehicles, is published.

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Section: Underwater Vehiclesmentioning

confidence: 99%

A Survey of Trajectory Planning Techniques for Autonomous Systems

et al. 2022

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“…Performance evaluation shows the effectiveness of the proposed algorithm for AUVs. Wenjie Chen et al [221] presented the solution to acquire quality images for localization underwater, as already presented algorithms such as SLAM (Simultaneous Localization and Mapping) do not have feature-based extraction quality, which often leads to blurry images. To cater to this issue, a new technique, visual SLAM using Generative Adversarial Networks (GANs), to improve the quality of images by evaluation metrics was introduced.…”

Section: Application To Underwater Vehiclesmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

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“…This line of research has mostly taken place in the computer vision fields, with the main focus on underwater single image restoration Akkaynak and Treibitz (2019); Islam et al (2020b). Benefiting from the superior performance of GAN-based approaches, some researchers attempted to use CycleGAN to boost the performance of ORB-SLAM in an underwater environment Chen et al (2019). The experimental results have shown that CycleGAN-based underwater image enhancement can lead to more matching points in a turbid environment.…”

Section: Introductionmentioning

confidence: 99%

Real-time GAN-based image enhancement for robust underwater monocular SLAM

Zheng

Zhou

et al. 2023

Front. Mar. Sci.

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Underwater monocular visual simultaneous localization and mapping (SLAM) plays a vital role in underwater computer vision and robotic perception fields. Unlike the autonomous driving or aerial environment, performing robust and accurate underwater monocular SLAM is tough and challenging due to the complex aquatic environment and the collected critically degraded image quality. The underwater images’ poor visibility, low contrast, and color distortion result in ineffective and insufficient feature matching, leading to the poor or even failure of the existing SLAM algorithms. To address this issue, we propose introducing the generative adversarial network (GAN) to perform effective underwater image enhancement before conducting SLAM. Considering the inherent real-time requirement of SLAM, we conduct knowledge distillation to achieve GAN compression to reduce the inference cost, while achieving high-fidelity underwater image enhancement and real-time inference. The real-time underwater image enhancement acts as the image pre-processing to build a robust and accurate underwater monocular SLAM system. With the introduction of real-time underwater image enhancement, we can significantly promote underwater SLAM performance. The proposed method is a generic framework, which could be extended to various SLAM systems and achieve various scales of performance gain.

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Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Cited by 10 publications

References 26 publications

A Survey of Trajectory Planning Techniques for Autonomous Systems

A Survey of Trajectory Planning Techniques for Autonomous Systems

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Real-time GAN-based image enhancement for robust underwater monocular SLAM

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