underwater mapping_2022

Underwater mapping 2022

Authors: Chensheng Cheng, Can Wang, Dianyu Yang, Weidong Liu and Feihu Zhang. SLAM (Simultaneous Localization And Mapping) plays a vital role in navigation tasks of AUV (Autonomous Underwater Vehicle). However, due to a vast amount of image sonar data and some acoustic equipment’s inherent high latency, it is challenging to implement real- time underwater SLAM on a small AUV. This paper presents a filter based methodology for SLAM algorithms in underwater environments.

Authors: Emir Cem Gezer, Lin Zhao, Jordan Beason, Mingxi Zhou. Date of publication: 2022 Seafloor mapping and monitoring are essential for creating a sustainable economy and widening our understanding of the ocean environments. However, the cost of conducting underwater operations has always been high. This paper presents the progress on developing an affordable Autonomous Under-water Vehicle (AUV), equipped with an interferometric sonar for seabed mapping at low altitudes or in coastal water.

Authors: Jinkun Wang, Fanfei Chen, Yewei Huang, John McConnell, Tixiao Shan & Brendan Englot Date of publication: 2022 This paper presents a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping (SLAM) with imaging sonar. The proposed system comprises path generation, place recognition forecasting, belief propagation, and utility evaluation using a virtual map, which estimates the uncertainty associated with map cells throughout a robot’s workspace.

Authors: Balint Z. Teglasy Emil Wengle, John R. Potter, and Sokratis Katsikas Secure digital wireless communication underwater is a crucial issue as the security of digital systems becomes challenged across all domains. The authors of this document address the requirements for security, namely, a confirmation of asset identities, also known as "authentication". They propose and validate an authentication protocol based on the first digital underwater communications standard primarily applicable to AUVs operating around offshore oil and gas facilities but also to other underwater devices.

Authors: Changho Yun To efficiently utilize nonexclusive underwater acoustic frequencies, The authors propose an Underwater Cooperative Spectrum Sharing (UCSS) protocol for a centralized underwater cognitive acoustic network consisting mainly of two parts. In the first part, to check the random occurrence of interferers periodically, the time domain is divided into frames that consist of a sensing and a non-sensing sub-frame. Then, they set the ratio of the two sub-frames to enhance the sensing rate via simulations.

Authors: Zheng Liu, Yaoming Zhuang, Pengrun Jia, Chengdong Wu, Hongli Xu, and Zhanlin Liu. The automatic detection and identification of marine organisms are critical for aquaculture resources. However, due to the low quality of underwater images and the characteristics of underwater biological detection, the lack of abundant features can impede traditional hand- designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex aquatic environments. Therefore, this study aimed to perform object detection in underwater environments.

Author: Serkan Aksoy This document explains notions such as sonar equation, acoustic waves, underwater acoustic equations, reverberation, Helmholtz equation, noise sources, cavitation, and target strength.

Author: Aslanbek Naziev The authors study the (in)dependence of additivity and homogeneity conditions in the definition of a linear mapping between vector spaces over the same scalar field. Unlike other works on this theme, which deal with particular fields like real or complex numbers, we consider the general case. That enables them to obtain an almost complete picture.

Authors: Tuochao Chen, Justin Chan, Shyamnath Gollakota The authors present a novel underwater system that can perform acoustic ranging between commodity smartphones. To achieve this, they design a real-time underwater ranging protocol that computes the time-of-flight between smartphones. To address the severe underwater multipath, they present a dual-microphone optimization algorithm that can more reliably identify the direct path. Their evaluations show that our system has median errors of 0.48–0.86 m at distances up to 35 m.

02 - A Comprehensive Comparison and Analysis of Several Intensity Modulations Based on the Underwater Photon- Counting Communication System

Authors: Xiaotian Han, Peng Li, Chang Chang, Duorui Gao, Dongquan Zhang, Peixuan Liao, Wei Wang, and Xiaoping Xie This document proposes solutions to the problems of underwater wireless optical communication, which is facing absorption and scattering problems.

Authors: Zhimin Li, Zibin Lin, Longsheng Zeng, Hao Wu, and Xue- Feng Zhu Manipulating underwater acoustic waves along the prescribed trajectory has excellent potential for various applications. Traditional metasurfaces for underwater acoustic modulation usually have complex structural designs and are complicated to manufacture. Here, we propose a simple strategy of embedding air bubbles of different sizes inside the polymer to manipulate the transmitted underwater acoustic wave fields freely.

16 - Marine priority habitat mapping in a Mediterranean conservation area (Gyaros, South Aegean) through multi-platform marine remote sensing techniques.

Authors: Xenophon Dimas, Elias Fakiris, Dimitris Christodoulou, Nikos Georgiou, Maria Geraga, Vasillis Papathanasiou, Sotiris Orfanidis, Spyros Kotomatas, and George Papatheodorou The aim of this study was to present the results of the first complete marine habitat mapping through marine remote sensing techniques in Gyaros Island, a remote island in the Cyclades archipelago with a great historical and ecological value

Author: Usman Ali, and Muhammad Tariq Mahmood The authors of this paper propose a method for underwater image restoration utilizing a technique that incorporates the potential structural differences between transmission maps and the guidance map.

Authors: Xu Liu, Sen Lin, Zhiyong Tao Evidence suggests that vision is among the most critical factors in marine information exploration. Instead, underwater images are generally of poor quality due to color casts, lack of texture details, and blurred edges. The authors propose the Multiscale Gated Fusion conditional GAN (MGF-cGAN) for underwater image enhancement. The generator of MGF-cGAN consists of Multiscale Feature Extract Module (Ms-FEM) and Gated Fusion Module (GFM). In Ms-FEM, they use three parallel subnets to extract feature information, which can extract richer features than a single branch.

08 - Underwater Hyperspectral Imaging of Arctic Macroalgal Habitats during the Polar Night Using a Novel Mini-ROV-UHI Portable System.

Authors: Natalie Summers, Geir Johnsen, Aksel Mogstad, Håvard Løvås, Glaucia Fragoso, Jørgen Berge This document describes an Underwater Hyperspectral Imager (UHI) deployed on an instrument-carrying platform consisting of two interconnected mini-ROVs (Remotely Operated Vehicle) for the mapping and monitoring of Arctic macroalgal habitats in Kongsfjorden (Svalbard) during the Polar Night.

Author: Hong-Gi Kim, Jungmin Seo, and Soo Mee Kim Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing recently, guaranteeing human safety and work efficiency. Optical cameras and multi-beam sonars are generally utilized as imaging sensors in underwater environments. However, the obtained underwater images are difficult to understand intuitively, owing to noise and distortion. This study explains the development of an optical and sonar image fusion system that integrates the color and distance information from two images.

Authors: Zhenjing Zhu, Ning Hu, Junyi Wu, Wenxin Li, Jiabao Zhao, Maofa Wang, Fanzong Zeng, Huajie Dai, and Yongju Zheng. This document presents a new method for estimating the angular pulse spectrum. The process is based on the Husimi transform of a wave ﬁeld and can be realized with a short vertical array of nondirectional hydrophones. As a result, one obtains a diagram of the arrival pattern in the time–angle plane. Special attention is paid to sound scattering on a cold synoptic eddy along the waveguide.

Authors: Denis V. Makarov, and Leonid E. Konkov This document presents a new method for estimating the angular pulse spectrum. The process is based on the Husimi transform of a wave field and can be realized with a short vertical array of nondirectional hydrophones. As a result, one obtains a diagram of the arrival pattern in the time–angle plane. Special attention is paid to sound scattering on a cold synoptic eddy along the waveguide.

Author: Yian Wang An underwater target ranging and size measurement system based on binocular vision is proposed to measure the distance and size information of underwater targets accurately. The underwater binocular calibration method is used to obtain the underwater parameters of the binocular camera, and the underwater images are calibrated to make them coplanar and aligned.

Authors: Boris Gasparovic, Jonatan Lerga, Goran Mausa, and Marina Ivasic-Kos This paper addresses the challenges posed by underwater conditions for image recognition and demonstrates the use of various Convolutional Neural Network (CNN) architectures, particularly focusing on the YOLO and Faster RCNN models. It evaluates these models based on detection accuracy, mean average precision, and processing speed, highlighting the effectiveness of the YOLOv4 model in achieving high accuracy and real-time detection capabilities. It also positions this work as a pioneering effort in the field of underwater pipeline detection using deep learning.

Click on the octopus to return to the top of the page

Author: Yaofeng Xie, Zhibin Yu, Xiao Yu, & Bing Zheng This article proposes a new underwater image enhancement network designed to address the challenges of enhancing images captured in low-light underwater environments. It explains the limitations of existing algorithms, the need for a specialized approach, and the creation of a new dataset to train the proposed model. It also highlights the effectiveness and robustness of the new method through experimental results, emphasizing its superior performance compared to previous methods.

Authors: Nicholas F. L. Vale, Juan C. Braga, Alex C. Bastos, Fernando C. Moraes, Claudia S. Karez, Ricardo G. Bahia, Luis A. Leão, Renato C. Pereira, Gilberto M. Amado-Filho, and Leonardo T. Salgado This document presents a study on rhodolith beds located on the Brazilian continental shelf, specifically off the Sergipe-Alagoas Coast. It describes the characteristics, distribution, and composition of these rhodolith beds, as well as the methods used for their study, such as ROV operations and bottom trawl sampling. It also provides information on the age and growth phases of the rhodoliths, highlighting their ecological significance and the diversity of red algal morpho-taxa identified in the area.

Authors: Kai Yan, Lanyue Liang, Ziqiang Zheng, Guoqing Wang, Yang Yang This text highlights the challenges of underwater visual perception due to factors like low illumination and suspended particles and critiques existing enhancement methods for their poor performance. It proposes a new approach that achieves better results with a simple and lightweight network configuration, demonstrating its effectiveness through experimental results. It also provides a link to access the code for this method, indicating an intent to share the research and facilitate further exploration or use by others.

Authors: Larissa Macedo Cruz de Oliveira, Aaron Lim, Luis A. Conti, and Andrew J. Wheeler This document describes a study that explores the use of machine learning methods combined with Structure-from- Motion (SfM) photogrammetry for the classification of cold-water coral (CWC) reefs in deep-water environments. It outlines the challenges faced in this domain, such as the lack of training data and benchmark datasets, and details the methodology used in the study, including the selection of the Piddington Mound area for data collection, the machine learning algorithms employed, and the evaluation of their performance.

29 - Present status and challenges of underwater acoustic target recognition technology: A review.

Authors: Lei Zhufeng, Lei Xiaofang, Wang Na, and Zhang Qingyang This study covers the generation of underwater target radiation noise and examines advancements in recognition techniques using machine learning in three areas: signal acquisition, feature extraction, and signal recognition. It also discusses the challenges in this field and proposes a new signal processing method that combines traditional techniques with deep learning to improve recognition rates and reduce computational costs.

24 - Battery-free wireless imaging of underwater environments

Authors: Sayed Saad Afzal, Waleed Akbar, Osvy Rodriguez, Mario Doumet, Unsoo Ha, Reza Ghaffarivardavagh, & Fadel Adib Underwater backscatter imaging is a scalable, real-time wireless method for underwater environments, enabling real-time observations of animals, plants, pollutants, and localization tags. This self-sustaining method is suitable for large, continuous deployments in oceans, with applications including marine life discovery, submarine surveillance, and underwater climate change monitoring. Existing methods require tethering for power and communication.

28 - Reinforcement Learning Based Mobile Underwater Localization for Silent UUV in Underwater Acoustic Sensor Networks

Authors: Ruiheng Liao, Wei Su, Xiurong Wu, and En Cheng This paper proposes a reinforcement learning and neural network-based mobile underwater localization scheme for unmanned underwater vehicles (UUVs) to improve localization accuracy and reduce energy consumption. The scheme uses SqueezeNet to select line-of-sight anchor nodes, while an RL-based approach selects anchor nodes without the underwater environment model. Simulation results show the proposed schemes significantly improve localization accuracy and reduce energy consumption.

Author: Eon-ho Lee, Byungjae Park, Myung-Hwan Jeon, Hyesu Jang, Ayoung Kim, Sejin Lee This paper discusses the challenges and solutions in object recognition for underwater unmanned vessels, particularly in regard to the limitations in obtaining experimental data. It introduces an image transformation model, Pix2Pix, to generate synthetic images that closely resemble real experimental data collected by the ROV SPARUS. These synthetic images are then used in conjunction with real sonar images to train a pixel segmentation deep learning model, FCN. The results indicate improved accuracy when incorporating synthetic data, highlighting its potential to alleviate the burdens of data preparation in underwater environments.

27 - A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

Authors: Timothy Sellers, Tingjun Lei, Chaomin Luo, Gene Eu Jan, Junfeng Ma This article discusses an innovative approach to autonomous robot multi-waypoint navigation and mapping, particularly for applications in search and rescue, environmental exploration, and disaster response. The proposed method utilizes an adjacent node selection (ANS) algorithm alongside a generalized Voronoi diagram (GVD) and an Improved Particle Swarm Optimization (IPSO) algorithm. This combination is aimed at creating safety-aware and efficient paths by bridging waypoints to the nearest nodes or edges in a graph, while also addressing obstacle avoidance through a sensor-based local reactive navigator.

Authors: Dandi Wang, Shuai Xing, Yan He, Jiayong Yu, Qing Xu, and Pengcheng This study introduces and evaluates a lightweight, dual- wavelength UAV-mounted airborne LiDAR bathymetry (ALB) system for shallow water mapping, tested at Dazhou Island, China. Using water surface points (fitted planes) and multibeam echosounder data as references, the system achieves decimeter-level precision: 0.1227 m for water surface fitting and 0.1268 m absolute bottom accuracy. It successfully identifies a 1m cube and resolves the rough shape of a 2 m cube on the seafloor at 12 m depth, demonstrating strong potential for flexible, high- resolution shallow-water mapping and underwater object detection.

Authors: Hongbo Yang, Zhizun Xu, and Baozhu Jia Underwater positioning is challenging due to electromagnetic wave attenuation, environmental disturbances, and terrain complexity. Acoustic methods suffer from low update rates, poor resolution, and noise sensitivity, while vision-based approaches struggle with sparse features, variable lighting, and light scattering. To address these issues, this paper proposes a novel visual- inertial-LiDAR system: a LiDAR camera provides robust depth maps via laser scanning, and an inertial measurement unit (IMU) supplies altitude data. Sensor fusion enables UUV position prediction, refined further using Bundle Adjustment to optimize rotation and translation estimates.

Authors: Gang Yang, Zhaoshuo Tian, Zongjie Bi, Zihao Cui, and Qingcao Liu In underwater LiDAR imaging, the conventional constant threshold adjacent frame difference (AFD) method loses target distance information due to the Gaussian distribution of laser light intensity, causing inhomogeneous intensity in images acquired by intensity charge-coupled devices (ICCD). To address this, the paper proposes the novel dynamic threshold adjacent frame difference (DTAFD) method, which adjusts the intensity threshold according to pixel intensities across different parts of a single frame ICCD image.

26 - LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors

Authors: Weichen Dai, Shenzhou Chen, Zhaoyang Huang, Yan Xu, and Da Kong LiDAR sensors provide illumination-robust depth and intensity data via pulsed lasers, but their sparse, non- camera-like intensity maps are underutilized. This work introduces LiDAR-Net, an end-to-end CNN that jointly completes sparse intensity and depth maps by leveraging their correlation. A novel intensity fusion method generates ground truth for training. Experiments show intensity–depth fusion improves completion performance, and downstream lane segmentation on completed intensity maps retains ambient-light robustness, enabling broader real-world LiDAR applications.

Authors: Guoqing Zhou, Xiang Zhou, Weihao Li, Dawei Zhao, Bo Song, Chao Xu, Haotian Zhang, Zhexian Liu, Jiasheng Xu, Gangchao Lin, Ronghua Deng, Haocheng Hu, Yizhi Tan, Jinchun Lin, Jiazhi Yang, Xueqin Nong, Chenyang Li, Yiqiang Zhao, Cheng Wang, Lieping Zhang, & Liping Zou This study introduces a lightweight bathymetry LiDAR prototype mounted on an unmanned shipborne vehicle to overcome limitations of traditional airborne LiDAR in narrow urban rivers and mountainous areas. The system includes key modules like emitting/receiving optics, control, and high-speed data processing.