  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.
