INTELLIGENT ROBOTS LAB.
CHUNGBUK NATIONAL UNIV.
Welcome to the Intelligent Robot Laboratory at Chungbuk National University. We focus on developing next-generation robotic technologies, with core expertise in SLAM, localization, and navigation for autonomous vehicles and AGVs. Our research bridges theory and real-world deployment, enabling intelligent systems to perceive and navigate complex environments. Discover our work through our publications, videos, and ongoing projects.
RECENT NEWS
[2025-05] Our latest research on real-time LiDAR loop closure detection for robust place recognition in challenging environments has been published in the Intelligent Service Robotics (ISR) journal. Read here.
[2025-05] Our research on a flexible multi-camera visual-inertial odometry system for robust localization in challenging ... has been published in the International Journal of Control, Automation, and Systems (IJCAS) journal. Give it a read here.
[2025-05] A new study from our lab on robust LiDAR-inertial localization for autonomous robots operating in challenging agricultural environments has been published in IEEE Robotics and Automation Letters (RA-L).
[2025-05] Congratulations to Hyundo Jung (PhD Research Scholar at IRL) for Best Paper Award at 21th Korea Robotics Society Annual Conference (KROC 2026).
IEEE RA-L
Published in IEEE RA-L, this work advances vision-language place recognition through attention-driven semantic and structured graph features. Read more.
2026-02
KROC Award
Hyundo Jung received the Best Paper Award at KROC 2026, recognizing outstanding research and contributions to robotics and autonomous systems.
2025-12
IEEE RA-L:
A recent paper published in IEEE Robotics and Automation Letters (RA-L) presents a proactive SLAM framework for improving localization robustness.
2025-08
IJCAS
Published in IJCAS, this work presents a flexible multi-camera visual-inertial architecture for robust state estimation in challenging environments. Full article.
Intelligent Service Robotics (ISR)
A new study introduces an efficient 3D LiDAR feature extraction and descriptor framework for robust and reliable loop closure detection. See publication.
IEEE RA-L:
A recent paper published in IEEE Robotics and Automation Letters (RA-L) presents a proactive SLAM framework for improving localization robustness.
2025-08
2026-02
2025-08
PROFESSOR
Kim, Gon-Woo, Ph.D.
College of Electrical & Computer Engineering
Intelligent Robots Lab. (IRL)
School of Electronics Engineering
Chungbuk National University
Tel: +82-43-261-2486
