Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

Abstract

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors

“…To further enhance the robustness and accuracy of DR systems, recent studies have explored the integration of factor graphs [19,20]. Zhang et al (2024) optimized AUV navigation by integrating side-scan sonar (SSS) data and inertial measurement units (IMU) using factor graphs, demonstrating significant improvements in positioning accuracy under challenging conditions [19].…”

“…To further enhance the robustness and accuracy of DR systems, recent studies have explored the integration of factor graphs [19,20]. Zhang et al (2024) optimized AUV navigation by integrating side-scan sonar (SSS) data and inertial measurement units (IMU) using factor graphs, demonstrating significant improvements in positioning accuracy under challenging conditions [19]. Similarly, Ma et al (2021) developed a multi-source information fusion algorithm based on factor graphs for AUVs, achieving enhanced computational efficiency and navigation performance by processing asynchronous heterogeneous measurements [20].…”