  Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and
Remotely Operated Vehicles (ROVs), are promising tools for collecting
biogeochemical data at the ice-water interface for scientific advancements.
However, state estimation, i.e., localization, is a well-known problem for
robotic systems, especially, for the ones that travel underwater. In this
paper, we present a tightly-coupled multi-sensors fusion framework to increase
localization accuracy that is robust to sensor failure. Visual images, Doppler
Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are
integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF)
for state estimation. Besides that a new keyframe-based state clone mechanism
and a new DVL-aided feature enhancement are presented to further improve the
localization performance. The proposed method is validated with a data set
collected in the field under frozen ice, and the result is compared with 6
other different sensor fusion setups. Overall, the result with the keyframe
enabled and DVL-aided feature enhancement yields the best performance with a
Root-mean-square error of less than 2 m compared to the ground truth path with
a total traveling distance of about 200 m.
