To meet the design and evaluation requirements of underwater vision-based docking localization, a Webots-based simulation platform for Autonomous Underwater Vehicle (AUV) visual docking localization was designed and implemented to address the high cost of real sea trials, uncontrollable operating conditions, and the difficulty of systematically covering extreme scenarios. An end-to-end simulation of the docking localization pipeline was provided. Visual components—including fiducial markers, underwater illumination and imaging, and occlusion—were modeled in relatively fine detail, while non-vision-dominant factors such as propulsion and hydrodynamics were treated with approximate models to balance visual realism and simulation efficiency. The platform supported multiple types of visual markers, parameterized configuration of underwater lighting and turbidity, and the generation of diverse occlusion scenarios, enabling unified integration and benchmarking of docking localization algorithms. The results showed that the platform offered tunable scene parameters, repeatable conditions, and broad algorithm compatibility, and it effectively revealed performance differences across algorithms for complex combinations of illumination, turbidity, and occlusion. These capabilities reduced the risk and cost of real underwater docking experiments and supported faster iterative improvement of vision-based localization methods.
Xing et al. (2026) studied this question.