Autonomous underwater docking is of the utmost importance for expanding the capabilities of Autonomous Underwater Vehicles (AUVs). Due to a historical focus on underwater docking to only static targets, the research gap in underwater docking to dynamically active targets has been left relatively untouched. We address the state estimation problem that arises when trying to rendezvous a chaser AUV with a dynamic target by modeling the scenario as a factor graph optimization-based Simultaneous Localization and Mapping problem. We present a set of boundary factors that aid the inference process by seamlessly transitioning the target’s state between the different observability stages, intrinsic to any dynamic docking scenario. We benchmark the performance of our approach using the Stonefish simulated environment.