Underwater mapping 2025
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16 - Cooperative SLAM Algorithm for Multi-AUV Underwater Exploration and Mapping
Author: Rongren Wu SLAM (Simultaneous Localisation and Mapping) is crucial for mapping unknown deep-sea areas. This paper suggests an AUV cluster SLAM algorithm to enhance mapping efficiency. It has three parts: (1) a multi-beam sonar image processing algorithm that filters dynamic points and redundant information; (2) SLAM using data from DVL, IMU, and DM based on a Rao-Blackwellised particle filter; and (3) an iUSBL system for cooperative positioning of AUVs. This algorithm significantly improves mapping efficiency and stabilizes SLAM performance by reducing errors from dynamic points.
15 - An overview of industrial image segmentation using deep learning models.
Authors: Guina Wang, Zhen Li, Guirong Weng, Yiyang Chen Image segmentation is important in AI and computer vision, used for tasks like industrial picking and video surveillance. This review covers traditional and deep learning segmentation algorithms, including their integration. It discusses various label frameworks and compares methods on benchmark datasets. Lastly, it addresses challenges and opportunities for future research.
01 - Remote Awareness of Image Quality for Multi-week Shore- launched AUV Surveys
Authors: Adrian Bodenmann, Daniel O.B. Jones, Alexander B. Phillips, Robert Templeton, Rashiid Sherif, Francesco Fanelli, Darryl Newborough, and Blair Thornton Visual seafloor imaging using autonomous underwater vehicles (AUVs) is now a common method for mapping and monitoring. This research creates a new way to assess image quality on-site during a 21-day AUV campaign. AUV operators received quality scores via satellite to improve data collection.
02 - Sound Absorption of the Water Column and Its Calibration for Multibeam Echosounder Backscattered Mapping in the East Sea of Korea
Authors: Seung-Uk Im, Cheong-Ah Lee, Moonsoo Lim, Changsoo Kim, and Dong-Guk Paeng This study uses Korea Oceanographic Data Center) datasets to analyze and visualize spatiotemporal variations in underwater sound absorption in the East Sea of Korea. The calibration process revealed discrepancies of up to 2.1 dB in backscatter intensity across survey lines, highlighting potential misrepresentation of legacy Multibeam echosounder (MBES) data due to site-specific absorption variability. This integrated approach enhances the reliability of legacy MBES data and provides valuable insights for marine resource management and environmental monitoring.
03 - Underwater DVL Optimization Network (UDON): A Learning- Based DVL Velocity Optimizing Method for Underwater Navigation
Authors: Feihu Zhang, Shaoping Zhao, Lu Li, and Chun Cao This paper proposes an end-to-end deep-learning approach to improve the accuracy of Doppler Velocity Log (DVL) velocity vector measurements for autonomous underwater vehicles (AUVs) that uses raw measurement data from an Inertial Measurement Unit (IMU) to assist DVL in velocity vector estimation and refining it towards the Global Positioning System (GPS). The research results show that the proposed method can achieve a maximum improvement of 69.26% in root mean square error and 78.62% in relative trajectory error in DVL vector predictions.
04 - Secrecy capacity maximization in autonomous underwater vehicle-enabled underwater acoustic sensor networks
Authors: Xufei Ding, Wen Tian, Xin Sun This study examines covert data collection in autonomous underwater vehicles (AUVs) for underwater acoustic sensor networks (UASNs). The researchers propose an efficient iterative optimization algorithm to optimize USN scheduling and AUV routes, aiming to maximize secrecy capacity under constraints such as AUV trajectory, USN scheduling, connection outage probability, and secrecy outage probability.
05 - Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network
Authors: Pan Xu, Dongbao Gao, Shui Yu, Guangming Li, Yun Zhao, and Guojun Xu This deep learning method is used to improve the physical spatial resolution of underwater sonar images using a proposed U-shaped end-to-end neural network, which includes down-sampling and up-sampling parts. This method reduces normalized loss on testing sets and improves the accuracy of predicted high-resolution images. This trained network can map high-resolution targets to impulse responses, providing a practical alternative for underwater target detection..
06 - Underwater Target Detection with High Accuracy and Speed Based on YOLOv10
Authors: Zhengliang Hu, Le Cheng, Shui Yu, Pan Xu, Peng Zhang, Rui Tian, and Jingqi Han This study presents an underwater target detection method based on YOLOv10 (You Only Look Once v10), which outperforms the YOLOv5 model in terms of detection accuracy and speed. The YOLOv10 model has a mAP50 (Average Precision at an Intersection over Union threshold of 50%) of 85.6% on the URPC 2020 (Underwater Robot Picking Contest 2020) dataset, a 1.2% improvement from YOLOv5, demonstrating its potential for precise and fast underwater target detection.
07 - Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11
Authors: Chang Zou, Siquan Yu, Yankai Yu, Haitao Gu, and Xinlin Xu The study proposes an improved YOLOv11 (You Only Look Once v11) network called YOLOv11-SDC for underwater object detection using side-scan sonar. It includes a new Sparse Feature module, a Dilated Reparam Block, a Control Module, and a Content-Guided Attention Fusion module. The improved method outperforms previous versions in detection performance.
08 - A Deep Shrinkage Network for Direction-of-Arrival Estimation with Sparse Prior
Authors: Lei Zhou, Shihong Zhou, Yubo Qi, Lixin Wu, and Yannick Benezeth This study proposes a deep learning-based framework for estimating Direction-of-arrival (DOA) in underwater multipath signals. It uses sparse representation and a super-resolution deep shrinkage reconstruction network (SDSR-Net) to map the sparse representation of the covariance matrix to the DOA. Simulations and evaluations show that the proposed method significantly improves DOA estimation accuracy.
09 - An Improved YOLOv9s Algorithm for Underwater Object Detection
Authors: Shize Zhou, Long Wang, Zhuoqun Chen, Hao Zheng, Zhihui Lin, and Li He The YOLOv9s-SD (You Only Look Once 9s-SD) underwater target detection algorithm aims to improve underwater object detection performance by combining MobileNetV2's (MobileNetV2 is a convolutional neural network architecture designed for mobile and embedded vision applications) inverted residual structure with Simple Attention Module and Squeeze-and-Excitation Attention. Experiments show improvement in mean Average Precision, demonstrating better adaptability to intricate underwater environments.
10 - Advancing Seabed Bedform Mapping in the Kuznica Deep: Leveraging Multibeam Echosounders and Machine Learning for Enhanced Underwater Landscape Analysis
Authors: Lukasz Janowski This study focuses on the development of an automatic seabed mapping methodology using multibeam echosounders and machine learning, integrating various scientific fields to understand the underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. It emphasizes using fewer, carefully selected features for training models, improving accuracy and robustness.
11 - Real-Time Registration of 3D Underwater Sonar Scans
Authors: António Ferreira, José Almeida, Aníbal Matos, and Eduardo Silva This article presents a collection of generic matrix manipulation kernels for parallelized perception applications. It discusses a parallel implementation for the 3DupIC algorithm, a probabilistic scan matching method for sonar scan registration. Tests show the algorithm's real-time performance, enabling 3D sonar scan matching to be executed onboard the EVA AUV.
12 - Multi-Scale Feature Enhancement Method for Underwater Object Detection
Authors: Mengpan Li, Wenhao Liu, Changbin Shao, Bin Qin, Ali Tian, and Hualong Yu This paper presents a Multi-Scale Feature Enhancement (MSFE) method to address challenges in underwater object detection, aiming to achieve dual multi-scale information integration through the internal structural design of the basic C2F module (a module that converts convolutional outputs into structured features) in the Backbone network and the external global design of the feature pyramid network (FPN). The method uses a module to address fine-grained feature vanishing and injects a shallow feature branch to enhance information integration between inter-layer features.
13 - The Method for Storing a Seabed Photo Map of the During Surveys Conducted by an Autonomous Underwater Vehicle
Authors: Chang Liu, Vladimir Filaretov, Eduard Mursalimov, Alexander Timoshenko, and Alexander Zuev This paper presents a new method for creating a photographic map of the seabed using images captured by autonomous underwater vehicles' photo and video systems, stored in a mosaic format, allowing for automatic search and recognition of underwater objects. The authors emphasize the efficiency of the method, which forms the map directly during the vehicle's movement, using only the onboard computer's computing power. Its algorithm for saving the map in mosaic format, used in interactive geographic maps like Google Maps, reduces read and write operations, ensuring timely operation.
14 - AUV Online Path Planning Strategy Based on Sectorial Gridded Detection Area
Authors: Yang Liu, Jinxi Sun, Guojie Li, and Xiujun Xu This study proposes an online path planning strategy for an autonomous underwater vehicle using forward- looking sonar in unknown environments. It divides the sonar fan-shaped detection area into multiple grids and uses a comprehensive cost function for optimal heading. This strategy ensures safe and smooth navigation in obstructed areas and narrow passages.
17 - Lightweight Underwater Target Detection Algorithm Based on YOLOv8n
Authors: Dengke Song and Hua Huo The RDL-YOLO is a lightweight detection algorithm designed to tackle challenges in underwater target detection, including complex environments, image blurring, and high model parameter counts that incorporates optimizations. Experimental results show an average precision increase of 1.4% and 1.0%, respectively, while reducing parameter count and computational complexity by 19.3% and 14.8%.
18 - Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artifi cial Landmarks
Authors: Jongdae Jung, Hyun-Taek Choi, and Yeongjun Lee This paper proposes a visual landmarks-based simultaneous localization and mapping (SLAM) system for autonomous underwater vehicles (AUVs). The system uses two types of underwater landmarks observed using forward and downward-looking cameras. The information, along with incremental DR estimates, is integrated within the extended Kalman filter (EKF) SLAM approach.
19 - SDA-Mask R-CNN: An Advanced Seabed Feature Extraction Network for UUV
Authors: Yao Xiao, Dongchen Dai, Hongjian Wang, Chengfeng Li, and Shaozheng Song The paper introduces a novel framework for precise seabed terrain edge feature extraction from Side-Scan Sonar (SSS) images, enhancing Unmanned Underwater Vehicle (UUV) perception and navigation. This framework addresses challenges in underwater image analysis, including low segmentation accuracy and ambiguous edge delineation. It incorporates a Structural Synergistic Group-Attention Residual Network, a Depth-Weighted Hierarchical Fusion Network, and an Adaptive Synergistic Mask Optimization strategy.
20 - A Holistic High-Resolution Remote Sensing Approach for Mapping Coastal Geomorphology and Marine Habitats
Authors: Evagoras Evagorou, Thomas Hasiotis, Ivan Theophilos Petsimeris, Isavela N. Monioudi, Olympos P. Andreadis, Antonis Chatzipavlis, Demetris Christofi, Josephine Kountouri, Neophytos Stylianou, Christodoulos Mettas, Adonis Velegrakis, and Diofantos Hadjimitsis This research demonstrates how satellite, aerial, terrestrial, and marine remote sensing techniques can be integrated to produce accurate information, revealing significant elevation and shoreline changes over one year, highlighting coastal zone dynamics. This highlights the importance of incorporating information from all available sensors for complete geomorphological and marine habitat mapping for sustainable coastal management strategies.
21 - Model-Based AUV Path Planning Using Curriculum Learning and Deep Reinforcement Learning on a Simplifi ed Electronic Navigation Chart
Authors: Lukasz Marchel, Rafał Kot, Piotr Szymak, and Paweł Piskur This paper evaluates reinforcement learning (RL)-based methods for autonomous underwater vehicles (AUVs) trajectory planning. It incorporates a full motion model and proposes a reward function to elicit desired behaviors. The models are trained on simplified ENC maps and compared against five classical methods. The proposed solution enables efficient, collision-free route planning.
22 - Depth Estimation of an Underwater Moving Source Based on the Acoustic Interference Pattern Stream
Authors: Lintai Rong, Bo Lei, Tiantian Gu, and Zhaoyang He This study proposes an interference feature fusion (IFF) method for underwater maneuvering sources in deep oceans that extracts the principal interference mode of the VTR and constructs a depth ambiguity function. This method achieves long-term depth estimation and recognition of underwater sources without environmental information. It can suppress false peaks and reduce depth estimation errors by at least 47% compared to the generalized Fourier transform method.
23 - Underwater Side-Scan Sonar Target Detection: An Enhanced YOLOv11 Framework Integrating Attention Mechanisms and a Bi-Directional Feature Pyramid Network
Authors: Junhui Zhu, Houpu Li, Min Liu, Guojun Zhai, Shaofeng Bian, Ye Peng, and Lei Liu Underwater target detection is crucial for marine exploration, but faces challenges due to the complex underwater environment. The ABFP-YOLO model addresses these issues by integrating a bi-directional feature pyramid network and an attention module. It effectively recognizes targets of varying scales, especially small ones, in complex scenarios. Experiments on two datasets show the model achieves mean average precision scores of 0.988 and 0.866, indicating superior performance in complex underwater environments.
24 - Oscillatory Forward-Looking Sonar Based 3D Reconstruction Method for Autonomous Underwater Vehicle Obstacle Avoidance
Authors: Hui Zhi, Zhixin Zhou, Haiteng Wu, Zheng Chen, Shaohua Tian, Yujiong Zhang, and Yongwei Ruan This study proposes a cost-effective 3D reconstruction method for autonomous underwater vehicle inspection in 3D environments that uses an oscillatory forward-looking sonar with a pan-tilt mechanism, extending perception from a 2D plane to a 75-degree spatial range. The system integrates with the Ego-Planner path planning algorithm and nonlinear Model Predictive Control algorithm, achieving 100%, 60%, and 30% obstacle avoidance success rates under localization errors.
25 - Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
Authors: Jaeed Bin Saif, Mohamed Younis, and Fow-Sen Choa This paper proposes a novel localization method using visible light communication (VLC) from an airborne unit to locate underwater nodes, which uses VLC transmissions towards the water surface, encoded with GPS coordinates. Existing techniques assume depth measurements, but this method uses VLC signals to estimate the underwater node's 3D position. Extensive simulations validate the method's effectiveness and performance across various parameters.
26 - Underwater SLAM Meets Deep Learning: Challenges, Multi- Sensor Integration, and Future Directions
Authors: Mohamed Heshmat, Lyes Saad Saoud, Muayad Abujabal, Atif Sultan, Mahmoud Elmezain, Lakmal Seneviratne, and Irfan Hussain This survey analyzes the latest developments in deep learning-enhanced simultaneous localization and mapping (SLAM) for underwater applications, categorizing approaches based on methodologies, sensor dependencies, and integration with deep learning models, highlighting the benefits and limitations of existing techniques, key innovations, and unresolved challenges. It also introduces a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs), enhancing localization, mapping, and real-time data sharing among AUVs.
27 - Underwater-Image Enhancement Based on Maximum Information-Channel Correction and Edge-Preserving Filtering
Authors: Wei Liu, Jingxuan Xu, Siying He, Yongzhen Chen, Xinyi Zhang, Hong Shu, and Ping Qi The authors propose an underwater-image enhancement (UIE) approach to address issues like color distortion and reduced contrast in underwater images. They use maximum information-channel compensation and edge- preserving filtering techniques to improve color quality. This approach involves enhancing the color-corrected image, generating globally contrast-enhanced images, and decomposing the enhanced image into low- frequency and high-frequency components.
28 - Task Allocation and Path Planning Method for Unmanned Underwater Vehicles
Authors: Feng Liu, Wei Xu, Zhiwen Feng, Changdong Yu, Xiao Liang, Qun Su, and Jian Gao This study proposes a task allocation and path planning method for Unmanned Underwater Vehicles (UUVs) to improve their autonomous navigation and adaptability in complex marine environments, using an Improved Grey Wolf Algorithm (IGWA) and a Circle chaotic mapping strategy to optimize task allocation and search efficiency. An algorithm termed GR-RRT is employed for path planning, ensuring obstacle avoidance and reducing redundant sampling. Experimental results show significant advantages in task completion rate and path optimization efficiency.
29 - YOLO-NeRFSLAM: underwater object detection for the visual NeRF-SLAM
Authors: Zhe Wang, Zhibin Yu, and Bing Zheng This paper proposes a new underwater Simultaneous Localization and Mapping (SLAM) framework that combines neural radiance fields (NeRF) with a dynamic masking module. The Marine Motion Fusion (MMF) strategy screens out dynamic objects, maintaining stable camera pose estimation and pixel-level dense reconstruction. Specialized loss functions cope with light attenuation and the dynamic nature of underwater scenes. Experimental results show that the method reduces localization drift, improves dense mapping accuracy, and achieves favorable runtime efficiency in multiple real underwater video datasets.
30 - Visual-Based Position Estimation for Underwater Vehicles Using Tightly Coupled Hybrid Constrained Approach
Authors: Tiedong Zhang, Shuoshuo Ding, Xun Yan, Yanze Lu, Dapeng Jiang, Xinjie Qiu, and Yu Lu This paper introduces a hybrid monocular visual Simultaneous Localization and Mapping (SLAM) system for unmanned underwater vehicles (UUVs), featuring a robust three-step tracking strategy. The system uses a feature-based method for rough pose estimation, a direct method for refining it, and a tightly coupled visual hybrid optimization method for stable tracking.
31 - Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining
Authors: Xinran Liu, Jianmin Yang, Changyu Lu, Enhua Zhang, and Wenhao Xu This study introduces a robust Forward-Looking Sonar (FLS) image mosaicking framework for real-time environmental mapping in deep-sea mining vehicles (DSMV). This model integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy and proposes a radial adaptive fusion algorithm for image fusion. The experimental results show high robustness and accuracy under real deep-sea conditions, supporting tasks like path planning, obstacle avoidance, and simultaneous localization and mapping, enabling reliable perceptual capabilities for intelligent underwater operations.
32 - Leveraging learned monocular depth prediction for pose estimation and mapping on unmanned underwater vehicles
Authors: Marco Job, David Botta, Victor Reijgwart, Luca Ebner, Andrej Studer, Roland Siegwart, and Eleni Kelasidi The paper introduces a framework that uses visual and acoustic sensor data to improve localization and mapping in complex underwater environments, particularly in fish farming. It enables net-relative pose estimation for Unmanned Underwater Vehicles (UUVs) and depth prediction using deep learning and sparse depth priors. This framework can generate detailed 3D maps in real- time, proving accurate for autonomous navigation and inspection.
33 - Direct Forward-Looking Sonar Odometry: A Two-Stage Odometry for Underwater Robot Localization
Authors: Wenhao Xu, Jianmin Yang, Jinghang Mao, Haining Lu, Changyu Lu, and Xinran Liu This paper proposes a lightweight front-end, forward- looking Sonar (FLS) odometry (DFLSO) for underwater robots, aiming to provide fast and accurate localization. It uses forward-looking sonar (FLS) images for pose estimation, extracting point clouds, and matching images. It also uses an image processing method to generate a 3- D point cloud, and a lightweight keyframe system to construct submaps, validated through simulation experiments and sea trials.