Sukjun Lee
Papers
2
Total Citations
7
H-Index
2
About
Sukjun Lee is a robotics researcher specializing in 3D perception and autonomous navigation for mobile platforms. His work focuses on enabling robots and drones to understand and interact with dynamic environments through deep learning-based object detection and scene understanding. Lee’s most cited paper, “Benchmark Analysis of Deep Learning-based 3D Object Detectors on NVIDIA Jetson Platforms” (2021, 5 citations), provides a critical evaluation of real-time 3D object detection for autonomous driving, demonstrating how one-shot inference of position, depth, and heading can generate reliable paths for robots. This work addresses the computational constraints of edge devices, making it highly relevant for practical deployment. His more recent study, “Scene Change Detection for Robotic Patrol System” (2024, 2 citations), advances patrol robotics by detecting semantic object variance in surveillance areas, enabling risk assessment during repeated monitoring. Unlike prior work limited to static environments, Lee’s approach adapts to dynamic settings, improving robot autonomy. Through these contributions, Lee is shaping the future of mobile robotics, bridging the gap between deep learning efficiency and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Scene Change Detection for Robotic Patrol System2 citations · 2024