InsSo3D: Inertial Navigation System and 3D Sonar SLAM for turbid environment inspection

2026-01-09

arXiv.org

AI Summary

This work addresses the challenge of accurate localization and mapping in turbid underwater environments, where conventional sonar lacks elevation information and visual methods fail. The paper presents the first large-scale 3D SLAM framework that fuses 3D sonar with an inertial navigation system (INS). By leveraging INS for motion priors and 3D sonar point clouds to capture environmental geometry, the system effectively corrects odometry drift through loop closure detection and pose graph optimization. This approach overcomes the elevation ambiguity inherent in 2D sonar. Evaluated on a 50-minute mission, the method achieves a trajectory average error below 21 cm and reconstructs a 10 m × 20 m map with an average error of 9 cm, significantly outperforming existing underwater motion tracking and visual structure-from-motion (SfM) techniques.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents InsSo3D, an accurate and efficient method for large-scale 3D Simultaneous Localisation and Mapping (SLAM) using a 3D Sonar and an Inertial Navigation System (INS). Unlike traditional sonar, which produces 2D images containing range and azimuth information but lacks elevation information, 3D Sonar produces a 3D point cloud, which therefore does not suffer from elevation ambiguity. We introduce a robust and modern SLAM framework adapted to the 3D Sonar data using INS as prior, detecting loop closure and performing pose graph optimisation. We evaluated InsSo3D performance inside a test tank with access to ground truth data and in an outdoor flooded quarry. Comparisons to reference trajectories and maps obtained from an underwater motion tracking system and visual Structure From Motion (SFM) demonstrate that InsSo3D efficiently corrects odometry drift. The average trajectory error is below 21cm during a 50-minute-long mission, producing a map of 10m by 20m with a 9cm average reconstruction error, enabling safe inspection of natural or artificial underwater structures even in murky water conditions.

Problem

• 3D SLAM
• turbid underwater environment
• 3D sonar
• inertial navigation
• underwater inspection

Innovation

• 3D Sonar
• Inertial Navigation System (INS)
• SLAM
• Loop Closure
• Underwater Mapping