Side-scan sonar based landmark detection for underwater vehicles

We propose and analyze a pipeline to transform raw sonar data from underwater vehicles into actionable information for Simultaneous Localization and Mapping (SLAM) in real time. The pipeline encompasses three sequential steps, each building upon state-of-the-art algorithms from the existing literature: swath processing to preprocess the raw sonar data, with a primary focus on eliminating blind zones and noise reduction; transformation of these swaths into probabilistic maps of the surroundings of the sonar; and finally, detection and classification of underwater landmarks from the probabilistic maps.Our work contributes by modifying algorithms from the literature to ensure computational efficiency and integrating them so that they operate in sequence, thereby furnishing valuable information for navigation purposes.Through validation with field data, we then discuss which scenarios may prove difficult for the individual stages of the proposed pipeline. We provide indications on whether each step may encounter challenges and discuss how this occurrence may affect the overall quality of the final result. This empirical discussion is useful for discerning the practical applicability of the proposed pipeline in real-world settings.