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1. CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration

   https://doi.org/10.1109/ICRA57147.2024.10611543  

   Abdullah, Adnan ; Barua, Titon ; Tibbetts, Reagan ; Chen, Zijie ; Islam, Md Jahidul ; Rekleitis, Ioannis ( May 2024 , IEEE)

   — In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e.g. caveline, arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through comprehensive benchmark analyses on cave systems in USA, Mexico, and Spain locations, we demonstrate that robust deep visual models can be developed based on CaveSeg for fast semantic scene parsing of underwater cave environments. In particular, we formulate a novel transformer-based model that is computationally light and offers near real-time execution in addition to achieving state-of-the-art performance. Finally, we explore the design choices and implications of semantic segmentation for visual servoing by AUVs inside underwater caves. The proposed model and benchmark dataset open up promising opportunities for future research in autonomous underwater cave exploration and mapping. 

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2. An Underwater SLAM System 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 novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization – one of the main problems affecting other packages in underwater domain – by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness. 

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3. Coral Identification and Counting with an Autonomous Underwater Vehicle

   https://doi.org/10.1109/ROBIO.2018.8664785  

   Modasshir, Md ; Rahman, Sharmin ; Youngquist, Oscar ; Rekleitis, Ioannis ( December 2018 , 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO))

   Monitoring coral reef populations as part of environmental assessment is essential. Recently, many marine science researchers are employing low-cost and power efficient Autonomous Underwater Vehicles (AUV) to survey coral reefs. While the counting problem, in general, has rich literature, little work has focused on estimating the density of coral population using AUVs. This paper proposes a novel approach to identify, count, and estimate coral populations. A Convolutional Neural Network (CNN) is utilized to detect and identify the different corals, and a tracking mechanism provides a total count for each coral species per transect. Experimental results from an Aqua2 underwater robot and a stereo hand-held camera validated the proposed approach for different image qualities. 

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4. Visual Tracking of Deepwater Animals Using Machine Learning-Controlled Robotic Underwater Vehicles

   Katija, K ; Roberts, PLD ; Daniels, J ; Lapides, A ; Barnard, K ; Risi, M ; Ranaan, BY ; Woodward, BG ; Takahashi, J ( January 2021 , IEEE Winter Conference on Applications of Computer Vision)

   null (Ed.)

   The ocean is a vast three-dimensional space that is poorly explored and understood, and harbors unobserved life and processes that are vital to ecosystem function. To fully interrogate the space, novel algorithms and robotic platforms are required to scale up observations. Locating animals of interest and extended visual observations in the water column are particularly challenging objectives. Towards that end, we present a novel Machine Learning-integrated Tracking (or ML-Tracking) algorithm for underwater vehicle control that builds on the class of algorithms known as tracking-by-detection. By coupling a multi-object detector (trained on in situ underwater image data), a 3D stereo tracker, and a supervisor module to oversee the mission, we show how ML-Tracking can create robust tracks needed for long duration observations, as well as enable fully automated acquisition of objects for targeted sampling. Using a remotely operated vehicle as a proxy for an autonomous underwater vehicle, we demonstrate continuous input from the ML-Tracking algorithm to the vehicle controller during a record, 5+ hr continuous observation of a midwater gelatinous animal known as a siphonophore. These efforts clearly demonstrate the potential that tracking-by-detection algorithms can have on exploration in unexplored environments and discovery of undiscovered life in our ocean. 

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5. Monocular Camera and Single-Beam Sonar-Based Underwater Collision-Free Navigation with Domain Randomization

   Yang, P. ; Liu, H. ; Roznere, M. ; Quattrini Li, A. ( January 2022 , International Symposium on Robotics Research (ISRR))

   Billard, A. ; Asfour, T. ; Khatib, O. (Ed.)

   Underwater navigation presents several challenges, including unstructured unknown environments, lack of reliable localization systems (e.g., GPS), and poor visibility. Furthermore, good-quality obstacle detection sensors for underwater robots are scant and costly; and many sensors like RGB-D cameras and LiDAR only work in-air. To enable reliable mapless underwater navigation despite these challenges, we propose a low-cost end-to-end navigation system, based on a monocular camera and a fixed single-beam echo-sounder, that efficiently navigates an underwater robot to waypoints while avoiding nearby obstacles. Our proposed method is based on Proximal Policy Optimization (PPO), which takes as input current relative goal information, estimated depth images, echo-sounder readings, and previous executed actions, and outputs 3D robot actions in a normalized scale. End-to-end training was done in simulation, where we adopted domain randomization (varying underwater conditions and visibility) to learn a robust policy against noise and changes in visibility conditions. The experiments in simulation and real-world demonstrated that our proposed method is successful and resilient in navigating a low-cost underwater robot in unknown underwater environments. The implementation is made publicly available at https://github.com/dartmouthrobotics/deeprl-uw-robot-navigation. 

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