# Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios

arXiv: https://arxiv.org/abs/2212.11671 · HTML: https://mlsanity.com/paper/2212.11671/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks](https://mlsanity.com/paper/1810.00825/) (2018)
- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements](https://mlsanity.com/paper/2210.11572/) (2022)
- [Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation](https://mlsanity.com/paper/2210.02930/) (2022)

## Built on by (most cited first, 7 of 7)

- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Seamless Underwater Navigation with Limited Doppler Velocity Log Measurements](https://mlsanity.com/paper/2404.13742/) (2024)
- [DCNet: A Data-Driven Framework for DVL Calibration](https://mlsanity.com/paper/2410.08809/) (2024)
- [DVL Calibration using Data-driven Methods](https://mlsanity.com/paper/2401.12687/) (2024)
- [Data-Driven Strategies for Coping with Incomplete DVL Measurements](https://mlsanity.com/paper/2401.15620/) (2024)
- [AUV Acceleration Prediction Using DVL and Deep Learning](https://mlsanity.com/paper/2503.16573/) (2025)
- [AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents](https://mlsanity.com/paper/2609.33299/) (2026)

## Abstract (arXiv)

Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and once reflected back, the AUV velocity vector can be estimated. However, in real-life scenarios, such as an uneven seabed, sea creatures blocking the DVL's view and, roll/pitch maneuvers, the acoustic beams' reflection is resulting in a scenario known as DVL outage. Consequently, a velocity update is not available to bind the inertial solution drift. To cope with such situations, in this paper, we leverage our BeamsNet framework and propose a Set-Transformer-based BeamsNet (ST-BeamsNet) that utilizes inertial data readings and previous DVL velocity measurements to regress the current AUV velocity in case of a complete DVL outage. The proposed approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV and was compared to a moving average (MA) estimator. Our ST-BeamsNet estimated the AUV velocity vector with an 8.547% speed error, which is 26% better than the MA approach.