Papers
7
Total Citations
185
H-Index
5
About
Yun Su is a robotics researcher whose work spans autonomous navigation, control systems, human-robot interaction, and multi-sensor fusion — with a particular focus on enabling robots to operate reliably in complex, unstructured environments. Su is perhaps best known for the GR-LOAM framework (2021, 93 citations), a LiDAR-based sensor fusion SLAM system designed specifically for ground robots navigating challenging terrain, a contribution that has become a significant reference point in mobile robotics research. Building on this, Su developed GR-Fusion and GR-SLAM, tightly coupled multi-sensor pipelines integrating LiDAR, camera, IMU, encoder, and GNSS data to achieve high robustness and low drift — addressing critical limitations of existing visual-inertial methods when applied to ground-based platforms. Su also contributed to the design and control of the innovative Scorpio EOD robot, a hybrid wheel-leg-track system, developing an adaptive nonlinear algorithm for self-balancing motion across variable environments. Further broadening the scope of their research, Su has explored human-robot interaction through heart rate variability as a measure of mental workload, tactile surface recognition using machine learning, and distributed multi-robot source-seeking strategies — demonstrating a versatile and impact-driven research portfolio.
Research Focus
Key Achievements
Top Papers
- 1GR-LOAM: LiDAR-based sensor fusion SLAM for ground robots on complex terrain93 citations · 2021
- 2Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot29 citations · 2019
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- 7A Novel Distributed Source Seeking Method Based on Multi-Robots Flocking3 citations · 2019