  Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs)
are used for a wide variety of missions related to exploration and scientific
research. Successful navigation by these systems requires a good localization
system. Kalman filter based localization techniques have been prevalent since
the early 1960s and extensive research has been carried out using them, both in
development and in design. It has been found that the use of a dynamic model
(instead of a kinematic model) in the Kalman filter can lead to more accurate
predictions, as the dynamic model takes the forces acting on the AUV into
account. Presented in this paper is a motion-predictive extended Kalman filter
(EKF) for AUVs using a simplified dynamic model. The dynamic model is derived
first and then it was simplified for a RexROV, a type of submarine vehicle used
in simple underwater exploration, inspection of subsea structures, pipelines
and shipwrecks. The filter was implemented with a simulated vehicle in an
open-source marine vehicle simulator called UUV Simulator and the results were
compared with the ground truth. The results show good prediction accuracy for
the dynamic filter, though improvements are needed before the EKF can be used
on real-time. Some perspective and discussion on practical implementation is
presented to show the next steps needed for this concept.
