Year: 2025
Type: article
Abstract: Recent advances in forward-looking sonar (FLS) have enabled the acquisition of high-resolution acoustic images. However, the accuracy of image-based rotation estimation remains limited owing to speckle noise, perceptual ambiguity, and shadows. In recent years, object-based path reconstruction has become increasingly important for underwater inspection tasks, and in such scenarios, reliably estimating rotation from static seabed objects is essential for ensuring the robustness of autonomous underwater vehicle (AUV) missions. Accordingly, we present a rotation estimation method that adaptively extracts a region of interest (ROI) and applies the Radon transform. The proposed approach automatically selects sonar image regions containing objects and emphasizes high projection values in the resulting sinogram. By computing the shift between the high projection values of two sinograms, the method achieves robust rotation estimation even under low contrast and severe speckle noise. Experimental results demonstrate that our method consistently achieves lower estimation errors than existing approaches, particularly in scenarios involving static seabed objects. These findings highlight its practical value for object-based path reconstruction, high-precision mapping, and other underwater navigation tasks.
DOI: 10.3390/jmse13122321
Authors: Hyeonmin Sim, Horyeol Choi, Hangil Joe
Institution: Kyungpook National University
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G443953093, G6925829959
OpenAlex ID: W4417108919
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