A Landmark-Aided Navigation Approach Using Side-Scan Sonar

2025-03-10

AI Summary

For autonomous underwater vehicles (AUVs) operating in GPS-denied underwater environments, this paper proposes a sidescan sonar-based landmark-aided navigation method enabling high-precision, real-time localization. The method establishes a hybrid Bayesian estimation framework integrating unscented transform (UT)-based state prediction with particle filter (PF)-based measurement update, explicitly modeling the strong nonlinearity of slant-range measurements and multi-source uncertainties. Probabilistic data association (PDA) is incorporated to robustly handle sonar detection-to-map landmark correspondence under clutter and uncertainty. This work presents the first synergistic use of UT and PF in sonar navigation—UT for efficient nonlinear prediction and PF for robust nonlinear update—balancing computational efficiency and estimation robustness. Extensive validation is conducted on synthetic data and field experiments across two heterogeneous AUV platforms equipped with distinct sonar systems and deployed in different marine environments. Results demonstrate significant convergence of positioning errors, confirming both methodological efficacy and practical potential for real-time deployment.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Cost-effective localization methods for Autonomous Underwater Vehicle (AUV) navigation are key for ocean monitoring and data collection at high resolution in time and space. Algorithmic solutions suitable for real-time processing that handle nonlinear measurement models and different forms of measurement uncertainty will accelerate the development of field-ready technology. This paper details a Bayesian estimation method for landmark-aided navigation using a Side-scan Sonar (SSS) sensor. The method bounds navigation filter error in the GPS-denied undersea environment and captures the highly nonlinear nature of slant range measurements while remaining computationally tractable. Combining a novel measurement model with the chosen statistical framework facilitates the efficient use of SSS data and, in the future, could be used in real time. The proposed filter has two primary steps: a prediction step using an unscented transform and an update step utilizing particles. The update step performs probabilistic association of sonar detections with known landmarks. We evaluate algorithm performance and tractability using synthetic data and real data collected field experiments. Field experiments were performed using two different marine robotic platforms with two different SSS and at two different sites. Finally, we discuss the computational requirements of the proposed method and how it extends to real-time applications.

Problem

• Develops cost-effective AUV navigation for ocean monitoring.
• Proposes Bayesian method for landmark-aided navigation using SSS.
• Evaluates algorithm with synthetic and real field data.

Innovation

• Bayesian estimation for landmark-aided navigation
• Unscented transform and particle-based update steps
• Real-time processing with Side-scan Sonar data