  Autonomous underwater vehicles (AUVs) are employed for marine applications
and can operate in deep underwater environments beyond human reach. A standard
solution for the autonomous navigation problem can be obtained by fusing the
inertial navigation system and the Doppler velocity log sensor (DVL). The
latter measures four beam velocities to estimate the vehicle's velocity vector.
In real-world scenarios, the DVL may receive less than three beam velocities if
the AUV operates in complex underwater environments. In such conditions, the
vehicle's velocity vector could not be estimated leading to a navigation
solution drift and in some situations the AUV is required to abort the mission
and return to the surface. To circumvent such a situation, in this paper we
propose a deep learning framework, LiBeamsNet, that utilizes the inertial data
and the partial beam velocities to regress the missing beams in two missing
beams scenarios. Once all the beams are obtained, the vehicle's velocity vector
can be estimated. The approach performance was validated by sea experiments in
the Mediterranean Sea. The results show up to 7.2% speed error in the vehicle's
velocity vector estimation in a scenario that otherwise could not provide an
estimate.
