Seongmin Lim

Korea University of Science and Technology

Papers

Total Citations

H-Index

About

Seongmin Lim is a researcher advancing the critical field of space situational awareness and active debris removal. His work centers on the intersection of deep learning, computer vision, and space robotics to address the growing challenge of orbital congestion. Lim’s most influential contribution, "Strategy for on-orbit space object classification using deep learning" (2021, 7 citations), pioneers the use of AI for real-time classification of space objects, a vital capability for autonomous satellite servicing and collision avoidance. This work directly supports the emerging paradigm of nanosatellite-based on-orbit servicing. Earlier, his foundational study "Vision-based Ground Test for Active Debris Removal" (2013, 4 citations) established critical experimental frameworks for testing visual navigation systems needed to rendezvous with and capture uncontrolled space debris. By bridging simulation and physical testing, Lim’s research provides the algorithmic and experimental groundwork for future missions that will safely remove hazardous debris. His contributions are essential for ensuring the long-term sustainability of space operations, making him a key voice in the next generation of space robotics and autonomous systems.