Abstract
Autonomous underwater vehicles (AUVs) are used in a wide range of underwater applications, ranging from seafloor mapping to industrial operations. While underwater, the AUV navigation solution commonly relies on the fusion between inertial sensors and Doppler velocity logs (DVL). To achieve accurate DVL measurements a calibration procedure should be conducted before the mission begins. Model-based calibration approaches include filtering approaches utilizing global navigation satellite system signals. In this paper, we propose an end-to-end deep-learning framework for the calibration procedure. Using stimulative data, we show that our proposed approach outperforms model-based approaches by 35% in accuracy and 80% in the required calibration time.
Original language | English |
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Title of host publication | OCEANS 2024 - Singapore, OCEANS 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350362077 |
DOIs | |
State | Published - 2024 |
Event | OCEANS 2024 - Singapore, OCEANS 2024 - Singapore, Singapore Duration: 15 Apr 2024 → 18 Apr 2024 |
Publication series
Name | Oceans Conference Record (IEEE) |
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ISSN (Print) | 0197-7385 |
Conference
Conference | OCEANS 2024 - Singapore, OCEANS 2024 |
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Country/Territory | Singapore |
City | Singapore |
Period | 15/04/24 → 18/04/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
ASJC Scopus subject areas
- Oceanography
- Ocean Engineering