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<p>Accurate localization of autonomous underwater vehicles (AUVs) is challenging because inertial measurement units (IMUs) and Doppler velocity logs (DVLs) accumulate drift during long-duration missions. To address this challenge, this study explores whether synthetic sonar imagery can provide transferable landmark cues for real-world mapping, thereby reducing dependence on costly and limited field datasets. Using an Unreal Engine&#xa0;5-based FURo-sim framework, we generated forward-looking sonar (FLS) data and automatically annotated high-intensity returns and their acoustic shadows. These annotations are used to train a YOLO11 detector exclusively on synthetic data, and the centroids of the resulting bounding boxes are interpreted as landmark observations. The landmark detections are then fused with DVL/IMU dead reckoning within a two-dimensional pose-graph optimization framework to refine both the vehicle trajectory and the resulting sonar mosaics. The proposed approach is validated using real coastal-sea data, with ultra-short baseline (USBL) acoustic positioning serving as a proxy for ground truth. Experimental results show that the method reduces dead-reckoning localization error by <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(15.4\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>15.4</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation>, demonstrating the effectiveness of the synthetic data-driven detector-based pose-graph formulation for real-world AUV navigation.</p>

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AUV pose correction via underwater object recognition using synthetic data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Jaemin Ha,
  • Heekyu Kweon,
  • Hyeonmin Sim,
  • Son-Cheol Yu,
  • Hangil Joe

摘要

Accurate localization of autonomous underwater vehicles (AUVs) is challenging because inertial measurement units (IMUs) and Doppler velocity logs (DVLs) accumulate drift during long-duration missions. To address this challenge, this study explores whether synthetic sonar imagery can provide transferable landmark cues for real-world mapping, thereby reducing dependence on costly and limited field datasets. Using an Unreal Engine 5-based FURo-sim framework, we generated forward-looking sonar (FLS) data and automatically annotated high-intensity returns and their acoustic shadows. These annotations are used to train a YOLO11 detector exclusively on synthetic data, and the centroids of the resulting bounding boxes are interpreted as landmark observations. The landmark detections are then fused with DVL/IMU dead reckoning within a two-dimensional pose-graph optimization framework to refine both the vehicle trajectory and the resulting sonar mosaics. The proposed approach is validated using real coastal-sea data, with ultra-short baseline (USBL) acoustic positioning serving as a proxy for ground truth. Experimental results show that the method reduces dead-reckoning localization error by \(15.4\%\) 15.4 % , demonstrating the effectiveness of the synthetic data-driven detector-based pose-graph formulation for real-world AUV navigation.