Taeyoung Uhm
Papers
13
Total Citations
96
H-Index
5
About
Taeyoung Uhm is a leading researcher at the intersection of robotics, multi-modal sensing, and smart city security. His work focuses on developing intelligent surveillance systems that integrate data from diverse agents—including CCTV, delivery robots, and unmanned shuttles—to create comprehensive environmental awareness. Uhm’s most impactful contribution is the “Multimodal layer surveillance map based on anomaly detection using multi‐agents for smart city security” (35 citations), which pioneers a framework for real-time anomaly detection in urban environments. He further advanced the field with the X-MAS dataset (10 citations), an extremely large-scale multi-modal sensor dataset designed for outdoor surveillance in real-world conditions. Uhm has also made notable strides in robotic navigation, proposing a flexible semantic ontological model that enables robots to understand and navigate large dynamic environments beyond simple metric mapping. His research extends to extreme environments, including a UWB-based human-following system for polar-exploration robots, integrating obstacle and crevasse avoidance. With over 90 total citations and a portfolio spanning multi-modal sensor calibration, outdoor security robots, and Antarctic exploration, Uhm’s work is foundational for building safer, more autonomous systems in complex, unstructured settings.
Research Focus
Key Achievements
Top Papers
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- 2Design of Multimodal Sensor Module for Outdoor Robot Surveillance System10 citations · 2022
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- 10Multi-modal Sensor Module for Antarctica Exploration Robots3 citations · 2023