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1. Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor

   Sharmin Rahman, Alberto Quattrini ( October 2019 , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper presents a systematic approach on realtime reconstruction of an underwater environment using Sonar, Visual, Inertial, and Depth data. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes the well defined edges between well lit areas and darkness to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a Visual SLAM system. Experimental results in an underwater cave at Ginnie Springs, FL, with a custommade underwater sensor suite demonstrate the performance of our system. This will enable more robust navigation of AUVs using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions. 

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2. Sonar Visual Inertial SLAM of Underwater Structures

   https://doi.org/10.1109/ICRA.2018.8460545  

   Rahman, Sharmin ; Li, Alberto Quattrini ; Rekleitis, Ioannis ( May 2018 , IEEE International Conference on Robotics and Automation (ICRA))

   This paper presents an extension to a state of the art Visual-Inertial state estimation package (OKVIS) in order to accommodate data from an underwater acoustic sensor. Mapping underwater structures is important in several fields, such as marine archaeology, search and rescue, resource management, hydrogeology, and speleology. Collecting the data, however, is a challenging, dangerous, and exhausting task. The underwater domain presents unique challenges in the quality of the visual data available; as such, augmenting the exteroceptive sensing with acoustic range data results in improved reconstructions of the underwater structures. Experimental results from underwater wrecks, an underwater cave, and a submerged bus demonstrate the performance of our approach. 

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3. Weakly Supervised Caveline Detection for AUV Navigation Inside Underwater Caves

   https://doi.org/10.1109/IROS55552.2023.10342435  

   Yu, Boxiao ; Tibbetts, Reagan ; Barua, Titon ; Morales, Ailani ; Rekleitis, Ioannis ; Islam, Md Jahidul ( October 2023 , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete absence of ambient light, which results in constantly moving shadows due to the motion of the camera-light setup. Thus, detecting and following the caveline as navigation guidance is paramount for robots in autonomous cave mapping missions. In this paper, we present a computationally light caveline detection model based on a novel Vision Transformer (ViT)-based learning pipeline. We address the problem of scarce annotated training data by a weakly supervised formulation where the learning is reinforced through a series of noisy predictions from intermediate sub-optimal models. We validate the utility and effectiveness of such weak supervision for caveline detection and tracking in three different cave locations: USA, Mexico, and Spain. Experimental results demonstrate that our proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring good generalization performance while offering 10+ FPS on single-board (Jetson TX2) devices. 

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4. SVIn2: A multi-sensor fusion-based underwater SLAM system

   https://doi.org/10.1177/02783649221110259  

   Rahman, Sharmin ; Quattrini Li, Alberto ; Rekleitis, Ioannis ( July 2022 , The International Journal of Robotics Research)

   This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.

    

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5. Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN

   https://doi.org/10.1109/JOE.2021.3086907  

   Panetta, Karen ; Kezebou, Landry ; Oludare, Victor ; Agaian, Sos ( January 2021 , IEEE Journal of Oceanic Engineering)

   Abstract—Current state-of-the-art object tracking methods have largely benefited from the public availability of numerous benchmark datasets. However, the focus has been on open-air imagery and much less on underwater visual data. Inherent underwater distortions, such as color loss, poor contrast, and underexposure, caused by attenuation of light, refraction, and scattering, greatly affect the visual quality of underwater data, and as such, existing open-air trackers perform less efficiently on such data. To help bridge this gap, this article proposes a first comprehensive underwater object tracking (UOT100) benchmark dataset to facilitate the development of tracking algorithms well-suited for underwater environments. The proposed dataset consists of 104 underwater video sequences and more than 74 000 annotated frames derived from both natural and artificial underwater videos, with great varieties of distortions. We benchmark the performance of 20 state-of-the-art object tracking algorithms and further introduce a cascaded residual network for underwater image enhancement model to improve tracking accuracy and success rate of trackers. Our experimental results demonstrate the shortcomings of existing tracking algorithms on underwater data and how our generative adversarial network (GAN)-based enhancement model can be used to improve tracking performance. We also evaluate the visual quality of our model’s output against existing GAN-based methods using well-accepted quality metrics and demonstrate that our model yields better visual data. Index Terms—Underwater benchmark dataset, underwater generative adversarial network (GAN), underwater image enhancement (UIE), underwater object tracking (UOT). 

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