Degradation-Aware Lightweight Monocular Six-Degree-of-Freedom Pose Estimation for Near-Field Underwater Charging Alignment

Abstract

Near-field underwater charging alignment requires six-degree-of-freedom (6-DoF) target pose estimates that remain geometrically stable under optical degradation while meeting onboard computational constraints. Attenuation, backscatter, non-uniform illumination, specular disturbance, and motion blur can weaken the local visual cues required for reliable monocular pose recovery. This paper presents a lightweight degradation-aware perception framework for near-field underwater charging alignment. The framework combines simplified physics-inspired underwater image synthesis, a lightweight high-resolution keypoint network, geometry-aware supervision with paired clean/degraded consistency learning, and quality-aware PnP pose recovery using underwater effective intrinsics. Evaluation includes a public 6-DoF reference benchmark, controlled low-light and motion-blur tests, the task-oriented Charging-UW dataset, and real underwater pool experiments. The controlled tests show substantially higher sensitivity to severe visibility loss than to the tested motion-blur range. In the 174-frame quantitative real-pool evaluation, the complete quality-aware pose-recovery procedure provides valid poses for all reference-valid frames, with median translation and rotation discrepancies of 3.71 cm and 3.08°, respectively, while the embedded inference pipeline operates at approximately 20 FPS on an NVIDIA Orin NX. These results demonstrate the feasibility of the proposed lightweight monocular pose-estimation framework for near-field underwater charging alignment. The present evaluation focuses on the perception stage preceding closed-loop insertion.