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

TL;DR

This paper introduces ST-BeamsNet, a Set-Transformer-based model that accurately estimates AUV velocity during complete DVL outages by leveraging inertial and past DVL data, outperforming traditional methods.

Contribution

The paper presents a novel Set-Transformer-based framework for AUV velocity estimation during DVL outages, improving accuracy over existing estimators.

Findings

ST-BeamsNet reduces velocity estimation error by 26% compared to moving average.

The approach effectively handles complete DVL outages in real underwater scenarios.

Experimental validation was conducted with the Snapir AUV in the Mediterranean Sea.

Abstract

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.