인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
The accurate registration of scanning sonar images is essential for underwater navigation and mapping, especially in environments where navigation sensors such as doppler velocity log or digital compasses are unreliable. Conventional sonar image registration methods often rely on navigation data or brightness-based keypoints, which are highly sensitive to intensity attenuation, reflection nonuniformity, and acoustic shadows, resulting in unstable correspondence under real underwater conditions. To overcome these limitations, this study proposes a structure-based registration algorithm reflecting the sensor characteristics of scanning sonar. The proposed method extracts structurally consistent feature points using azimuthal peak-response sampling and selects stable anchor points through multiscale accumulation. An uncertainty-weighted triplet structural hash descriptor is then constructed to model geometric consistency while suppressing unreliable correspondences. Reliable inlier pairs are identified without relying on any navigation data using k-dimensional tree based nearest neighbor search and mutual-consistency filtering. the experimental results demonstrate that the proposed method achieves a mean registration error of 4.6 pixels in translation and 0.29 °in rotation. These results confirm that the proposed algorithm can robustly align sonar images even under severe intensity distortions and shadow regions, providing a foundation for structure-based underwater image registration that reflects sonar sensor characteristics.
본문·목차
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