Vortrag: Modular Multi-Sensor Fusion for Underwater Localization for Autonomou...
Stammdaten
Titel: | Modular Multi-Sensor Fusion for Underwater Localization for Autonomous ROV Operations |
Beschreibung: | Localization filters for underwater vehicles are mostly tailored for specific sensor suites, environments, or missions. It is also well known that the underwater environment can evolve over time and throughout the mission, affecting the vehicle's sensors, e.g., tide, currents, and vehicle proximity to structures, especially in harbor areas. In this paper, the Modular and Robust Sensor-Fusion Framework (MaRS) is extended to work with underwater vehicles and their environment. It enables efficient use of asynchronous sensors and handles measurement outliers and outages. Sensor-frame initialization and online extrinsic calibration methods are also explored. Tests are performed in real harbor-like environments using a small remotely operated vehicle (ROV) and show improved handling of sensors and state estimation results. |
Schlagworte: | ROV Navigation, State-Estimation, EKF, Sensor-Fusion |
Typ: | Angemeldeter Vortrag |
Homepage: | https://hamptonroads22.oceansconference.org/ |
Veranstaltung: | OCEANS 2022 Hampton Roads Conference (Virginia Beach, VA) |
Datum: | 20.10.2022 |
Vortragsstatus: | stattgefunden (Präsenz) |
Beteiligte
Martin Scheiber (intern) |
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Alexandre Cardaillac (extern) |
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Christian Brommer (intern) |
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Stephan Michael Weiss (intern) |
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Martin Ludvigsen (extern) |
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Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Intelligente Systemtechnologien
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AT - 9020 Klagenfurt am Wörthersee |
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Klassifikationsraster der zugeordneten Organisationseinheiten:
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Publiziert? |
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Kooperationen
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Norwegian University of Science and Technology
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NO
- 7491
Trondheim |
Forschungsaktivitäten
(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
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