Effective monitoring of underwater ecosystems is crucial for tracking environmental changes, guiding conservation efforts, and ensuring long-term ecosystem health. However, automating underwater ecosystem management with robotic platforms remains challenging due to the complexities of underwater imagery, which pose significant difficulties for traditional visual localization methods. We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes. Furthermore, we introduce the SQUIDLE+ VPR Benchmark-the first large-scale underwater VPR benchmark designed to leverage an extensive collection of unstructured data from multiple robotic platforms, spanning time intervals from days to years. The dataset encompasses diverse trajectories, arbitrary overlap and diverse seafloor types captured under varying environmental conditions, including differences in depth, lighting, and turbidity. Our code is available at: https://github.com/bev-gorry/underloc
University of South Carolina（南卡罗来纳大学）；Dartmouth College（达特茅斯学院）
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 refi…
Tianjin University（天津大学）；State Administration of Cultural Heritage（国家文物局）
Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD). Despite the fruitful achievements that ACR can support, it still remains a challenging problem caused by the unstable results of relative pose estimation, especially for outdoor scenes, where the lighting condition is out of control, i.e., the twice observations may have highly varied illuminations. This paper studies an illumination-invariant active camera relocalization method, it…
Automatic Coral Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring
Cadi Ayyad University（卡迪·阿雅德大学）；University of La Réunion（留尼汪大学）；IRD（法国国家可持续发展研究所）；IFREMER（法国海洋开发研究院）；CNRS（法国国家科学研究中心）；University of New Caledonia（新喀里多尼亚大学）；Institut de Recherche pour le Développement（法国国家可持续发展研究所）；MNHN（法国国家自然历史博物馆）；EPHE（巴黎高等研究实践学院）
Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation.This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple object…
Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges
OceanScan Marine Systems & Technology（OceanScan海洋系统与技术公司）；INESC INOV-Lab（INESC INOV实验室）；ISRC (ISEP/P.PORTO)（ISRC（波尔图高等工程学院/波尔图大学））；Faculty of Engineering, University of Porto (FEUP)（波尔图大学工程学院（FEUP））
DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Coding
Robotics Innovation Center, German Research Center for Artificial Intelligence（德国人工智能研究中心机器人创新中心）；Robotics Research Group, University of Bremen（不莱梅大学机器人研究组）；Computing Department, SENAI CIMATEC（SENAI CIMATEC计算部门）
Queensland University of Technology（昆士兰科技大学）；Data61, CSIRO（联邦科学与工业研究组织数据61研究所）
Monitoring coral reefs using underwater vehicles increases the range of marine surveys and availability of historical ecological data by collecting significant quantities of images. Analysis of this imagery can be automated using a model trained to perform semantic segmentation, however it is too costly and time-consuming to densely label images for training supervised models. In this letter, we leverage photo-quadrat imagery labeled by ecologists with sparse point labels. We propose a point label aware method for propagating labels within superpixel regions to obtain augmented ground truth fo…
SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology
College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；Beijing Ratu Technology Co., Ltd.（北京瑞途科技有限公司）
GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
Southwest University of Science and Technology（西南科技大学）；Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；McGill University（麦吉尔大学）
We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that currently perform monitoring tasks in unstructured natural environments navigate via path-tracking a pre-specified sequence of waypoints. Although this navigation method is often necessary, it is limiting because the robot does not have a model of what the scientist deems to be relevant visual observations. Thus, the robot can neither visually search for particular types of objects, nor focus its attention on parts …
YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa（电子电气与航海工程系（DITEN），热那亚大学）；Department of Bio-systems Engineering, Gyeongsang National University（生物系统工程系，全州国立大学）；Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences（机械工程与航海研究系，西挪威应用科学大学）
University of Maryland Institute for Advanced Computer Studies（马里兰大学高级计算机研究所）
Oysters are the living vacuum cleaners of the oceans. There is an exponential decline in the oyster population due to over-harvesting. With the current development of the automation and AI, robots are becoming an integral part of the environmental monitoring process that can be also utilized for oyster reef preservation. Nevertheless, the underwater environment poses many difficulties, both from the practical - dangerous and time consuming operations, and the technical perspectives - distorted perception and unreliable navigation. To this end, we present a simulated environment that can be use…
DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring
Electrical Engineering, University of South Florida（佛罗里达州立大学电气工程系）；Maryland Robotics Center (MRC), University of Maryland（马里兰大学机器人中心）；Woods Hole Oceanographic Institution (WHOI)（伍兹霍尔海洋研究所）；Mechanical Engineering, University of Delaware（德雷克塞尔大学机械工程系）
The BioRobotics Institute, Sant’Anna School of Advanced Studies（圣安娜高等研究院生物机器人研究所）；Sant’Anna School of Advanced Studies（圣安娜高等研究院）；Healthcare Engineering Innovation Center, Khalifa University（哈利法大学医疗工程创新中心）；Autonomous and Robotics Research Centre, Technology Innovation Institute（技术创新研究院自主与机器人研究中心）；College of Engineering, Khalifa University（哈利法大学工程学院）
Water monitoring is crucial for environmental monitoring, transportation, energy and telecommunication. One of the main problems in aquatic environmental monitoring is biofouling. The simplest method among the current antifouling strategies is the use of wiper technologies like brushes and wipers which apply mechanical pressure. In designing built-in strategies however, manufacturers usually build the sensor around the biofouling system. The current state-of-the-art is a fully integrated central wiper in the sensor that enables cleaning of all probes mounted on the sonde. Improvements in antif…
This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We valid…
Digital Twins Below the Surface: Enhancing Underwater Teleoperation
School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）；School of Mathematics and Computer Sciences, Heriot-Watt University（赫瑞瓦特大学数学与计算机科学学院）
Dehazing-aided Multi-Rate Multi-Modal Pose Estimation Framework for Mitigating Visual Disturbances in Extreme Underwater Domain
Center for Autonomous Robotic Systems, Khalifa University（哈利法大学自主机器人系统中心）；College of Engineering, Abu Dhabi University（阿布扎比大学工程学院）；Autonomous Robotics Research Center, Technology Innovation Institute（技术创新研究院自主机器人研究中心）
Visually-guided underwater robots are deployed alongside human divers for cooperative exploration, inspection, and monitoring tasks in numerous shallow-water and coastal-water applications. The most essential capability of such companion robots is to visually interpret their surroundings and assist the divers during various stages of an underwater mission. Despite recent technological advancements, the existing systems and solutions for real-time visual perception are greatly affected by marine artifacts such as poor visibility, lighting variation, and the scarcity of salient features. The dif…
ICM-CSIC（西班牙加泰罗尼亚海洋科学研究所）；Khalifa University（哈利法大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；KTH Royal Institute of Technology（皇家理工学院）；Scuola Superiore Sant’Anna（圣安娜高等学院）
CUREE: A Curious Underwater Robot for Ecosystem Exploration
Woods Hole Oceanographic Institution (WHOI)（伍兹霍尔海洋研究所）；MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering（麻省理工学院-伍兹霍尔海洋研究所海洋学/应用海洋科学与工程联合项目）
ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics
Maryland Robotics Center, University of Maryland（马里兰大学马里兰机器人中心）；Center for Autonomous and Robotic Systems, University of Delaware（特拉华大学自主与机器人系统中心）；Automation and Control Institute, TU Wien（维也纳技术大学自动化与控制研究所）；School of Marine Science and Policy, University of Delaware（特拉华大学海洋科学与政策学院）；University of Maryland Center for Environmental Science（马里兰大学环境科学中心）；College of Engineering and Applied Science, University of Cincinnati（辛辛那提大学工程与应用科学学院）；Indepedendent Robotics（独立机器人公司）
OASIS: Real-Time Opti-Acoustic Sensing for Intervention Systems in Unstructured Environments
Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Deep Submergence Laboratory（应用海洋物理与工程，伍兹霍尔海洋研究所，深潜实验室）；Massachusetts Institute of Technology（麻省理工学院）
Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its perfo…
This paper addresses the problem of robotic operations in the presence of adversarial forces. We presents a complete framework for survey operations: waypoint generation,modelling of forces and tuning the control. In many applications of environmental monitoring, search and exploration, and bathymetric mapping, the vehicle has to traverse in straight lines parallel to each other, ensuring there are no gaps and no redundant coverage. During operations with an Autonomous Surface Vehicle (ASV) however, the presence of wind and/or currents produces external forces acting on the vehicle which quite…
