# DCNet: A Data-Driven Framework for DVL Calibration

arXiv: https://arxiv.org/abs/2410.08809 · HTML: https://mlsanity.com/paper/2410.08809/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Variants of RMSProp and Adagrad with Logarithmic Regret Bounds](https://mlsanity.com/paper/1706.05507/) (2017)
- [Overfitting Mechanism and Avoidance in Deep Neural Networks](https://mlsanity.com/paper/1901.06566/) (2019)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [Adaptive Kalman-Informed Transformer](https://mlsanity.com/paper/2401.09987/) (2024)
- [Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios](https://mlsanity.com/paper/2212.11671/) (2022)
- [MissBeamNet: Learning Missing Doppler Velocity Log Beam Measurements](https://mlsanity.com/paper/2301.11597/) (2023)
- [Seamless Underwater Navigation with Limited Doppler Velocity Log Measurements](https://mlsanity.com/paper/2404.13742/) (2024)
- [INS/DVL Fusion with DVL Based Acceleration Measurements](https://mlsanity.com/paper/2308.11762/) (2023)
- [DVL Calibration using Data-driven Methods](https://mlsanity.com/paper/2401.12687/) (2024)

## Built on by (most cited first, 4 of 4)

- [Data-Driven Gyroscope Calibration](https://mlsanity.com/paper/2410.12485/) (2024)
- [Neural-Assisted in-Motion Self-Heading Alignment](https://mlsanity.com/paper/2604.00168/) (2026)
- [DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation](https://mlsanity.com/paper/2606.23502/) (2026)
- [Information-Aided DVL Calibration](https://mlsanity.com/paper/2606.31216/) (2026)

## Abstract (arXiv)

Autonomous underwater vehicles (AUVs) are underwater robotic platforms used in a variety of applications. An AUV's navigation solution relies heavily on the fusion of inertial sensors and Doppler velocity logs (DVL), where the latter delivers accurate velocity updates. To ensure accurate navigation, a DVL calibration is undertaken before the mission begins to estimate its error terms. During calibration, the AUV follows a complex trajectory and employs nonlinear estimation filters to estimate error terms. In this paper, we introduce DCNet, a data-driven framework that utilizes a two-dimensional convolution kernel in an innovative way. Using DCNet and our proposed DVL error model, we offer a rapid calibration procedure. This can be applied to a trajectory with a nearly constant velocity. To train and test our proposed approach a dataset of 276 minutes long with real DVL recorded measurements was used. We demonstrated an average improvement of 70% in accuracy and 80% improvement in calibration time, compared to the baseline approach, with a low-performance DVL. As a result of those improvements, an AUV employing a low-cost DVL, can achieve higher accuracy, shorter calibration time, and apply a simple nearly constant velocity calibration trajectory. Our results also open up new applications for marine robotics utilizing low-cost, high-accurate DVLs.