Seungsang Yun

Korea Advanced Institute of Science and Technology, Seoul National University

Papers

Total Citations

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About

Seungsang Yun is a robotics researcher whose work focuses on advancing LiDAR-based simultaneous localization and mapping (SLAM) and thermal perception for autonomous systems. His most-cited paper, "SC-LiDAR-SLAM: A Front-end Agnostic Versatile LiDAR SLAM System" (2022, 33 citations), introduces a modular SLAM framework that decouples front-end odometry from back-end optimization, enabling robust 3D point cloud map generation for diverse robotic missions and urban analysis. This contribution addresses a critical challenge in robotics: creating accurate, adaptable SLAM systems that work across different sensor configurations and environments. Yun also explores multimodal perception in "Night-to-day thermal image translation for deep thermal place recognition" (2023), where he applies domain adaptation techniques to enable reliable place recognition under low-light conditions using thermal cameras. His work bridges the gap between theoretical SLAM algorithms and practical deployment in challenging real-world scenarios, such as nighttime navigation or degraded visual environments. With growing citation impact and a focus on versatile, sensor-agnostic solutions, Yun is contributing to the next generation of robust autonomous navigation systems.