Research Analyzer
← Back ICRA 2026

Geometric Correction of Underwater Forward Looking Sonar-Based 3-D Reconstruction Via PS-Based Slope Pattern Interpretation Using AUV

Seungwon Ham, Bonchul Ku, Young-woon Song, Jason Kim, You hyun Jang, WOOJIN SEOL, Son-Cheol Yu

PDF

AI summary

Key figure (auto-extracted from paper)
Leveraging profiling sonar slope patterns to correct forward-looking sonar ambiguity effectively eliminates falsely inclined surfaces and boosts 3D reconstruction accuracy without trajectory changes.
Forward-looking sonar Underwater 3D reconstruction Geometric correction Profiling sonar AUV navigation Elevation ambiguity

Problem

Forward-looking sonar 3D reconstruction suffers from elevation ambiguity that creates falsely inclined surfaces, while existing correction methods require impractical repeated observations.

Approach

A pattern-informed geometric refinement framework uses profiling sonar slope patterns to identify ambiguous intervals and selectively correct the FLS-based 3D model.

Key results

  • Suppression of falsely inclined surfaces in FLS reconstructions
  • Preservation of accurate geometric structures for sloped and vertical objects
  • 18–20% error reduction on sloped surfaces and 32–52% on vertical surfaces
  • Single-pass correction without requiring AUV trajectory modifications

Why it matters

Enables reliable, real-time underwater 3D mapping for AUVs in complex environments where repeated scanning is impractical.

Abstract

Forward-looking sonar (FLS) enables long-range underwater sensing. In FLS-based 3-D reconstruction, falsely inclined surfaces arise from elevation ambiguity caused by the finite vertical beamwidth. Existing approaches mitigate these errors using multi-pass strategies, but they require repeated observations, which are often impractical in real- world underwater operations. To address this, we propose a pattern-informed geometric refinement framework that lever- ages structural patterns from profiling sonar (PS) to resolve ambiguity in FLS-based reconstruction. Within this framework, geometric patterns within ambiguity-dominated intervals are analyzed to distinguish between physically valid surfaces and falsely inclined surfaces, and selective geometric refinement is applied accordingly. Experimental results demonstrate ef- fective suppression of falsely inclined surfaces and improved reconstruction accuracy without trajectory modifications. This provides a practical solution for reliable 3-D mapping and perception in underwater robotic applications. Sonar Imaging, Underwater Mapping, Geometric Correc- tion, AUV.

Index terms

Marine Robotics Mapping Range Sensing

Related papers