  Autonomous underwater vehicles (AUV) are commonly used in many underwater
applications. Recently, the usage of multi-rotor unmanned autonomous vehicles
(UAV) for marine applications is receiving more attention in the literature.
Usually, both platforms employ an inertial navigation system (INS), and aiding
sensors for an accurate navigation solution. In AUV navigation, Doppler
velocity log (DVL) is mainly used to aid the INS, while for UAVs, it is common
to use global navigation satellite systems (GNSS) receivers. The fusion between
the aiding sensor and the INS requires a definition of step size parameter in
the estimation process. It is responsible for the solution frequency update
and, eventually, its accuracy. The choice of the step size poses a tradeoff
between computational load and navigation performance. Generally, the aiding
sensors update frequency is considered much slower compared to the INS
operating frequency (hundreds Hertz). Such high rate is unnecessary for most
platforms, specifically for low dynamics AUVs. In this work, a supervised
machine learning based adaptive tuning scheme to select the proper INS step
size is proposed. To that end, a velocity error bound is defined, allowing the
INS/DVL or the INS/GNSS to act in a sub-optimal working conditions, and yet
minimize the computational load. Results from simulations and field experiment
show the benefits of using the proposed approach. In addition, the proposed
framework can be applied to any other fusion scenarios between any type of
sensors or platforms.
