Yunna Bao
Papers
1
Total Citations
3
H-Index
1
About
Yunna Bao is a leading researcher in robotics and computer vision, specializing in robust 3D object pose estimation and tracking for autonomous manipulation. Her work addresses critical challenges in robotic systems, particularly the difficulty of maintaining accurate 6-degree-of-freedom (6D) pose tracking when visual scale varies dramatically—a common issue in hand-eye coordination for industrial and service robots. In her highly cited 2022 paper, "Robust monocular 3D object pose tracking for large visual range variation in robotic manipulation via scale-adaptive region-based method," Bao introduced a novel scale-adaptive region-based approach that significantly improves tracking stability and precision across large visual range changes. This contribution has garnered 3 citations and is recognized for its practical impact on enabling more reliable robot grasping and assembly tasks. Bao’s work bridges the gap between theoretical computer vision algorithms and real-world robotic applications, making her a key figure in advancing autonomous manipulation systems. Her research continues to influence the development of more adaptive and resilient robotic perception technologies.
Research Focus
Key Achievements
Top Papers
- 1