  Due to the challenges regarding the limits of their endurance and autonomous
capabilities, underwater docking for autonomous underwater vehicles (AUVs) has
become a topic of interest for many academic and commercial applications.
Herein, we take on the problem of state estimation during an autonomous
underwater docking mission. Docking operations typically involve only two
actors, a chaser and a target. We leverage the similarities to proximity
operations (prox-ops) from spacecraft robotic missions to frame the diverse
docking scenarios with a set of phases the chaser undergoes on the way to its
target. We use factor graphs to generalize the underlying estimation problem
for arbitrary underwater prox-ops. To showcase our framework, we use this
factor graph approach to model an underwater homing scenario with an active
target as a Simultaneous Localization and Mapping problem. Using basic AUV
navigation sensors, relative Ultra-short Baseline measurements, and the
assumption of constant dynamics for the target, we derive factors that
constrain the chaser's state and the position and trajectory of the target. We
detail our front- and back-end software implementation using open-source
software and libraries, and verify its performance with both simulated and
field experiments. Obtained results show an overall increase in performance
against the unprocessed measurements, regardless of the presence of an
adversarial target whose dynamics void the modeled assumptions. However,
challenges with unmodeled noise parameters and stringent target motion
assumptions shed light on limitations that must be addressed to enhance the
accuracy and consistency of the proposed approach.
