This study presents an innovative method for estimating the height of feature points through shaded area analysis, to enhance the performance of iterative closest point (ICP)-based algorithms for matching scanning sonar images. Unlike other sensors, such as forward looking sonar (FLS) or BlueView, scanning sonar has an extended data acquisition period, complicating data collection while in motion. Additionally, existing ICP-based matching algorithms that rely on two-dimensional scanning sonar data suffer from matching errors due to ambiguities in the nearest-point matching process, typically arising when the feature points demonstrate similarities in size and spatial arrangement, leading to numerous potential connections between them. To mitigate these matching ambiguities, we restrict the matching areas in the two images that need to be aligned. We propose two strategies to limit the matching area: the first utilizes the position and orientation information derived from the navigation algorithm, while the second involves estimating the overlapping region between the two images through height assessments of the feature points, facilitated by shaded area analysis. This latter strategy emphasizes preferential matching based on the height information obtained. We propose integrating these two approaches and validate the proposed algorithm through simulations, experimental basin tests, and real-world data collection, demonstrating its effectiveness.

서 론 오늘날 상당수의 수중작업(수중구난, 해양과학탐사, 해저유전 개발 등)에 무인수중잠수정(Unmanned underwater vehicle, UUV) 이 지속적으로 사용되어오고 있으며, 이들 작업은 수중에서의 조작(Manipulation) 기능까지 요구한다. 수중작업에서 가장 일반 적으로 사용되고 있는 ROV(Remotely operated vehicle)는 수상의 선박에 탑승한 2명 이상의 숙련된 운용자에 의해 조종된다. 이 중 한명은 ROV 몸체를, 나머지 한명은 로봇팔을 각각 전담한다. ROV는 선상의 갑판 위에 진⋅회수장치 및 전력공급장치, DPS (Dynamic positioning system) 등을 갖춘