A Controlled Simulation Study of Degeneracy-Aware Pose Fusion in Confined Metal Compartments

Geometric degeneracy and poor illumination make localization difficult in confined metal compartments. This study examines quality-dependent pose fusion in a four-dimensional position-and-yaw model using known-map LiDAR registration, synthetic visual poses, and noisy velocity and yaw-rate inputs. A geometric-visual quality score controls the combination of iterative closest point (ICP) and normal distributions transform (NDT) pose observations, while modality-specific covariance scales and innovation checks regulate state updates. Across 60 controlled Monte Carlo trials, the full configuration achieves a mean position error of 0.029 m, a between-trial standard deviation of 0.003 m, a mean position root-mean-square error of 0.034 m, and a mean peak error of 0.112 m. Frames in error runs above 0.15 m lasting at least five samples account for 0.00%. Representative processing time is 25.65 ms per frame, excluding synthetic generation. Paired ablations identify LiDAR innovation screening as the main measured accuracy contributor; independent mean-accuracy benefits from covariance and mixing adaptation are not established. Conditional NDT reduces registration work. Sustained common pose-observation corruption raises the mean error from 0.066 m at 6 s to 0.178 m at 24 s, and a fixed reduced configuration remains competitive. A separate field point-cloud study evaluates compact map representation. These findings characterize controlled pose-fusion behavior; sensor-level LiDAR-visual-inertial performance and real-compartment trajectory accuracy require independently referenced validation.