Yihuai Gao
Papers
1
Total Citations
4
H-Index
1
About
Yihuai Gao is a rising researcher in robotics and computer vision, whose work centers on uncertainty quantification for 6D pose estimation—a critical challenge for autonomous systems operating in the real world. In their highly cited 2024 paper, "CLOSURE: Fast Quantification of Pose Uncertainty Sets," Gao tackles the problem of rigorously bounding the possible poses of an object when measurements from learned models (such as keypoints or pose hypotheses) are noisy. Assuming unknown-but-bounded measurement noise, Gao introduces a method to compute a Pose Uncertainty Set (PURSE)—a subset of SE(3) that contains all 6D poses compatible with the measurements. This work, which has already garnered 4 citations in its first year, provides a fast, principled framework for certifying the reliability of pose estimates, a crucial step for safe manipulation and navigation. By moving beyond point estimates to quantify uncertainty, Gao’s contributions are laying the groundwork for more trustworthy perception systems in robotics, where knowing what you don’t know is just as important as knowing the answer.
Research Focus
Key Achievements
Top Papers
- 1CLOSURE: Fast Quantification of Pose Uncertainty Sets4 citations · 2024