DVL Calibration Using Data-Driven Methods

Zeev Yampolsky, Itzik Klein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationOCEANS 2024 - Singapore, OCEANS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350362077
DOIs
StatePublished - 2024
EventOCEANS 2024 - Singapore, OCEANS 2024 - Singapore, Singapore
Duration: 15 Apr 202418 Apr 2024

Publication series

NameOceans Conference Record (IEEE)
ISSN (Print)0197-7385

Conference

ConferenceOCEANS 2024 - Singapore, OCEANS 2024
Country/TerritorySingapore
CitySingapore
Period15/04/2418/04/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

ASJC Scopus subject areas

  • Oceanography
  • Ocean Engineering

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