[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84755-en":3,"doc-seo-84755-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84755,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","DIVO Continuous-time DVL-Inertial-Visual Odometry for Unmanned Underwater Vehicles","DIVO presents an acoustic–visual–inertial odometry approach for unmanned underwater vehicles using a continuous-time trajectory estimation framework. It addresses underwater localization and mapping issues such as light attenuation, illumination variation, and particulate matter that degrade visual feature tracking. The method performs multi-sensor fusion of asynchronous Doppler velocity log, stereo camera, and inertial measurement unit signals using a Gaussian-process continuous-time backend. A learning-based visual frontend improves feature extraction and matching for subsea conditions. Extensive experiments on real underwater inspection datasets show higher accuracy, robustness, and trajectory coverage than visual-inertial and acoustic-visual-inertial SLAM.","DIVO: Continuous-time DVL-Inertial-Visual Odometry for Unmanned Underwater Vehicles  \nKyungmin Jung, Angad Bajwa, Junha Yoo, Arturo Del Castillo Bernal, and James Richard Forbes  \narXiv :2607 .046 15v 1 [ cs .RO] 6 Jul 2026  \nAbstract—This paper presents a novel acoustic-visual-inertialodometry solution leveraging a continuous-time trajectory estimation framework for unmanned underwater vehicles. Underwater environments present unique challenges for visual localization and mapping, such as light attenuation, illumination variance, and the presence of particulate matter. This motivates the use of additional sensing modalities and a visual tracking pipeline that is robust to diverse subsea conditions. The proposed system is the first continuous-time trajectory estimation framework based on Gaussian processes to fuse asynchronous measurements from a Doppler velocity log, a stereo camera, and an inertial measurement unit. Additionally, a novel visual frontend is proposed, incorporating learning-based feature extraction and matching that is robust to the specific challenges that subsea environments present. The proposed framework enables seamless integration of additional sensor modalities in continuous-time and is adaptable to different environments without reconfiguration. The proposed system is extensively tested on real-world underwater inspection datasets, where it outperforms state-of-the-art visual-inertial and acoustic-visual-inertial SLAM algorithms in accuracy, robustness, and trajectory coverage. Notably, the proposed system outperforms the state-of-the-art despite only forming short-term visual data associations.  \nIndex Terms—underwater SLAM, multi-sensor fusion, Doppler velocity log, Continuous-time estimation  \nI. INTRODUCTION  \nUNMANNED underwater vehicles (UUVs) have been  \nwidely used in underwater inspection, mapping, and exploration [1] . The success of these missions relies heavily on accurate and robust localization and mapping capabilities. Considering that most UUVs are equipped with cameras, visual simultaneous localization and mapping (SLAM) techniques have been extensively studied for underwater applications [2–4] .  \nDespite great success above water [5–7], visual-only SLAM faces unique challenges in underwater environments. Poor visibility caused by light attenuation limits the range of visual feature detection, dynamic lighting conditions due to causticsand turbidity cause tracking failure, and the presence of particulate matter such as suspended sediments and organic detritus, known as “marine snow\", can occlude and distract visual  \nThis work was supported in part by Voyis Imaging Inc. through the Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance program, the NSERC Discovery Grant program, and in part by the McGill Engineering Doctoral Award (MEDA) Program at McGill University.  \nThe authors are with the Department of Mechanical Engineering, McGill University, Montreal, QC, Canada, H3A 0C3 .  \n{kyungmin . jung, angad .bajwa, junha .yoo, arturo .delcastillobernal}@mail .mcgill .ca, [james.richard.forbes@mcgill.ca](james.richard.forbes@mcgill.ca)  \nFig. 1: The ROV’s trajectory estimated by the proposed DIVO method (blue) is overlaid on top of the ground truth trajectory (black) and the mesh generated using Agisoft Metashape. The proposed system can robustly register images in visually challenging scenes such as low visibility and high presence of dynamic particles. As an example, a sample of images at the beginning of the trajectory are shown.  \nfeatures. Their performance is further compromised in featurescarce environments such as open water and seabeds [3] .  \nTo overcome these challenges, fusing visual data with inertial measurement unit (IMU) data has been proposed and shown to improve the robustness of underwater visual SLAM [8, 9] . Visual-inertial SLAM leverages the ego-motion constraints provided by the IMU to aid pose estimation, but only functions under visual failure","cbCaie7M0BLYyU35","https://ap.wps.com/l/cbCaie7M0BLYyU35","pdf",22109781,2,1,20,"English","en",105,"# Abstract\n# I. 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