Side-Scan Sonar SLAM Using Ping-Level Landmark Detection in Feature-Poor Seabed Environments

Abstract

Side-scan sonar (SSS) is a particularly attractive sensing modality for underwater simultaneous localization and mapping (SLAM), offering wide-area seabed coverage and reliable acoustic measurements over long ranges. Many existing SSS-based SLAM approaches rely on image-domain processing, which depends on sufficiently rich image features and can struggle in feature-poor or homogeneous seabed environments. Furthermore, even when image-domain features are present, range-dependent intensity variations and speckle noise inherent in SSS measurements can degrade the reliability of feature extraction and data association. This study proposes a ping-level SSS SLAM framework that directly exploits raw backscatter intensity profiles without relying on image formation. By characterizing the nominal seafloor response and identifying structurally salient deviations in the acoustic intensity profiles, reliable landmark measurements are extracted at the ping level and incorporated into a landmark-based SLAM framework. This formulation preserves the native sensing geometry of SSS measurements and enables robust landmark extraction even in feature-sparse environments. The proposed approach is validated through real-world field experiments, demonstrating improved robustness and localization accuracy in challenging seabed conditions.