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    Item type:Publication, Access status: with files ,
    ASV-aided AUV navigation: A field study on nonlinear estimation for localization of low-cost, scalable systems
    (IEEE, 2025-11-27)
    Turrisi, Raymond
    ;
    Dücker, Daniel-André
    ;
    Morrison, John
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    Steinmetz, Fabian
    ;
    Benjamin, Michael
    This work investigates the use of multiple Autonomous Surface Vehicles (ASVs) as Communication/Navigation Aids (CNAs) to enhance the navigation and state estimation of an Autonomous Underwater Vehicle (AUV). Our approach builds on recent advancements in low-cost sensors and platforms, which enable novel AUV applications across fundamental science, commercial industries, and defense. We consider six different combinations of Kalman Filter and Factor Graph localization solutions on three datasets, covering 53 minutes and 3.1 kilometers of operation. We first present the solution using the measurements from all three ASVs, before occluding measurements from two of the ASVs to assess the effect of reduced observability on localization performance.
    Publicationtype: Conference Paper
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    Item type:Publication, Access status: without files ,
    Experimental open-source framework for underwater pick-and-place studies with lightweight UVMS – an extensive quantitative analysis
    (IEEE, 2025-10)
    Bauschmann, Nathalie
    ;
    Lenz, Vincent
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    Seifried, Robert
    ;
    Dücker, Daniel-André
    The rise of lightweight, low-cost underwater vehicle-manipulator systems (UVMS) has made autonomous underwater manipulation increasingly accessible. Yet, most current research remains limited to isolated tasks, such as trajectory tracking or compensation of unknown payloads. Detailed experimental analyses that go beyond a proof-of-concept are particularly rare.We present a comprehensive open-source software framework for fully automated pick-and-place studies. We build upon our previous work on a task-priority control framework and extend it to enable fully autonomous manipulation. This includes a high-level decision-making process to coordinate the pick-and-place sequence and a grasp detection method to verify the successful pick-up of the object. We demonstrate this framework on the widely-used platform of a BlueROV2 and an Alpha 5 manipulator.Extensive quantitative experimental studies (100+ trials) show the picking and placing to be highly accurate, with mean position errors of <5 mm and <10 mm, respectively. We additionally validate our grasp detection approach and analyze trajectory tracking sensitivity to varying payloads and speeds. These results provide a baseline of what accuracy is currently achievable with state-of-the-art lightweight hardware under ideal research conditions. The code is available at https://github.com/HippoCampusRobotics/uvms.
    Publicationtype: Conference Paper
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