  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.
