Noise-Robust Acoustic Navigation for AUVs via Advanced Signal Processing and Likelihood Field Estimation

Abstract

To improve the efficiency of underwater structure inspections, the deployment of Autonomous Underwater Vehicles (AUVs) is highly anticipated. However, accurate self-localization relative to feature-poor structures remains challenging due to the cumulative errors of dead reckoning (DR). Furthermore, acoustic data from mechanical scanning sonars contain severe noise caused by multipath reflections and suspended particles, hindering reliable feature extraction. This paper proposes a novel framework integrating robust signal processing of acoustic sonar data and hierarchical selflocalization. First, multi-stage filtering combining soft gain adjustment, a Laplacian filter, and dynamic thresholding using CA-CFAR successfully extracted only the surface edges of the structure as a clear point cloud, even under severe environmental noise. Next, by introducing a computationally efficient analytical rectangular model, we integrated a “high-frequency update” via a likelihood field-based MCL per beam and a “lowfrequency update” via Likelihood Field Optimization using KD-Tree and L-BFGS-B algorithms per scan. In a concrete block inspection experiment using the AUV Tuna-Sand2 in an actual sea environment, the proposed method successfully reduced the localization error (RMSE) by approximately 75% compared to the uncorrected DR trajectory, demonstrating its effectiveness for stable long-term autonomous navigation.