The research addresses underwater challenges including optical "foggy vision", electromagnetic "signal isolation" and scarce training data. By fusing visual and acoustic sensors, the team builds an "ocean AI brain" for autonomous robot navigation and task execution. CHINGMU's system provides critical ground truth for robot pose and trajectory, comprehensively verifying localization algorithm accuracy under complex motions such as sharp turns and sudden stops.