Quality-Adaptive Multimodal Localization for an Aquaculture-Vessel Bulkhead-Cleaning Robot: A Measurement-Informed Software-in-the-Loop Feasibility Study

Reliable wall-relative localization is required for robotic cleaning of aquaculture-vessel bulkheads, where sensor availability changes across the air–water interface. A two-stage study separated measured dry-wall performance from a controlled evaluation of cross-medium estimation logic. In Stage I, five coordinate-estimation models (P0–P4) were evaluated at 23 non-initial control points on an 8 m × 4 m vertical steel wall. The hard-axis-constrained WO/IMU model P0 achieved an RMSE of 0.1806 m and a maximum error of 0.3818 m on the predominantly axis-aligned trajectories. In Stage II, five localization models (M1–M5) were evaluated over four prescribed scenarios and 400 paired software-in-the-loop realizations. All methods used the same simulated sensor streams and initial position; no reference-derived auxiliary velocity was supplied during estimation. Medium- and visual-quality adaptation reduced the 95th percentile of per-run maximum error from 0.5480 m for the fixed-parameter filter to 0.3756 m for M4 and reduced the 95th percentile of per-run maximum excess position jumps to approximately 0.151 m. M5 used encoder–IMU heading disagreement only to inflate process uncertainty and achieved an aggregate ATE-RMSE of 0.1024 m, which was practically indistinguishable from the 0.1028 m obtained by M4. Its remaining benefit was confined mainly to modest improvements in transition error and recovery behavior under prescribed slip. The results demonstrate estimator behavior in a measurement-informed feasibility study; synchronized tank and vessel experiments remain necessary.