Pose Estimation of Unmanned Underwater Vehicles Using Augmented Reality Marker-Based Simulations

This study presents a simulation-based framework for pose estimation of Unmanned Underwater Vehicles (UUVs) using a monocular vision system within a ROS-Gazebo environment. The RexRov2 UUV model, integrated with ArUco_ROS, is used to detect virtual markers and estimate position and orientation in a simulated underwater setting. A Perspective-n-Point (PnP) method is applied for pose estimation, and a proportional-integral-derivative (PID) controller regulates vehicle motion based on marker-derived features. The system is evaluated by comparing estimated poses with ground-truth odometry obtained from the simulator. Under nominal conditions, the results demonstrate stable pose estimation with close agreement between estimated and true positions and orientations. The system maintains smooth trajectory tracking with minimal fluctuations, indicating reliable performance in controlled environments. Under increased hydrodynamic disturbances, however, the system exhibits deviations in position and orientation, leading to instability in tracking performance. These results highlight the limitations of classical PID control in nonlinear underwater environments and suggest the need for more robust control strategies. Overall, the proposed framework provides a safe, flexible, and cost-effective platform for testing underwater navigation algorithms and evaluating perception-control integration in simulated environments.