# DVL Calibration using Data-driven Methods

arXiv: https://arxiv.org/abs/2401.12687 · HTML: https://mlsanity.com/paper/2401.12687/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [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)
- [Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation](https://mlsanity.com/paper/2210.02930/) (2022)
- [ProNet: Adaptive Process Noise Estimation for INS/DVL Fusion](https://mlsanity.com/paper/2212.08882/) (2022)
- [INS/DVL Fusion with DVL Based Acceleration Measurements](https://mlsanity.com/paper/2308.11762/) (2023)

## Built on by (most cited first, 1 of 1)

- [DCNet: A Data-Driven Framework for DVL Calibration](https://mlsanity.com/paper/2410.08809/) (2024)

## Abstract (arXiv)

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.