BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation

TL;DR

This paper introduces BeamsNet, a deep learning framework that significantly improves DVL velocity vector estimation for AUV navigation, potentially replacing traditional model-based methods with data-driven accuracy enhancements.

Contribution

BeamsNet is a novel end-to-end deep learning approach that enhances DVL velocity estimation accuracy using either combined or separate sensor data inputs.

Findings

Achieved over 60% improvement in velocity vector estimation accuracy.

Validated through both simulation and real sea experiments.

Demonstrated potential to replace traditional model-based DVL processing.

Abstract

Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided by a Doppler velocity log (DVL) is used to provide the vehicle's navigation solution. In such fusion, the DVL provides the velocity vector of the AUV, which determines the navigation solution's accuracy and helps estimate the navigation states. This paper proposes BeamsNet, an end-to-end deep learning framework to regress the estimated DVL velocity vector that improves the accuracy of the velocity vector estimate, and could replace the model-based approach. Two versions of BeamsNet, differing in their input to the network, are suggested. The first uses the current DVL beam measurements and inertial sensors data, while the other utilizes only DVL data, taking the current and past DVL measurements for the regression process. Both simulation and sea experiments were made to validate the proposed learning approach relative to the model-based approach. Sea experiments were made with the Snapir AUV in the Mediterranean Sea, collecting approximately four hours of DVL and inertial sensor data. Our results show that the proposed approach achieved an improvement of more than 60% in estimating the DVL velocity vector.