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항법 적용을 위한 수중 소나 영상 처리 요소 기법 비교 분석
신영식(Young-Sik Shin),조영근(Younggun Cho),이영준(Yeongjun Lee),최현택(Hyun-Taek Choi),김아영(Ayoung Kim) 한국해양공학회 2016 韓國海洋工學會誌 Vol.30 No.3
Imaging sonars such as side-scanning sonar or forward-looking sonar are becoming fundamental sensors in the underwater robotics field. However, using sonar images for underwater perception presents many challenges. Sonar images are usually low resolution with inherent speckled noise. To overcome the limited sensor information for underwater perception, we investigated preprocessing methods for sonar images and feature detection methods for a nonlinear scale space. In this paper, we focus on a comparative analysis of (1) preprocessing for sonar images and (2) the feature detection performance in relation to the scale space composition.
자율 수중 로봇을 위한 사실적인 실시간 고밀도 3차원 Mesh 지도 작성
이정우(Jungwoo Lee),조영근(Younggun Cho) 한국로봇학회(논문지) 2024 로봇학회 논문지 Vol.19 No.2
This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.