Donghun Noh
Papers
6
Total Citations
96
H-Index
4
About
Donghun Noh is a robotics researcher whose work spans perception, manipulation, locomotion, and energy efficiency in autonomous systems. His most impactful contribution, “Analysis and Noise Modeling of the Intel RealSense D435 for Mobile Robots” (61 citations), provides a foundational benchmark for depth camera performance—a critical resource for researchers deploying low-cost RGB-D sensors as alternatives to expensive LIDAR. Noh has also advanced task planning for dual-arm robots, developing mixed-integer programming frameworks that optimize multi-step cooking tasks, enabling safe and efficient coordination in shared workspaces. In the realm of novel locomotion, he co-developed BALLU2, a buoyancy-assisted biped that never falls, offering a paradigm shift in safe, lightweight walking robots. More recently, his work on power consumption analysis for humanoid welcome robots addresses the practical challenge of battery life in deployed service robots. Noh’s research consistently bridges hardware characterization, algorithmic planning, and real-world deployment, making him a versatile contributor to modern robotics.
Research Focus
Key Achievements
Top Papers
- 1Analysis and Noise Modeling of the Intel RealSense D435 for Mobile Robots61 citations · 2019
- 2
- 3BALLU2: A Safe and Affordable Buoyancy Assisted Biped8 citations · 2021
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- 6