Papers
2
Total Citations
35
H-Index
2
About
Xinghan Li is a leading researcher in robotic state estimation, with a focus on leveraging geometric Lie group theory and ultra-wideband (UWB) sensing to solve fundamental challenges in autonomous navigation. His work bridges the gap between theoretical rigor and practical deployment. Li’s most impactful contribution is the development of a closed-form error propagation formula for the Invariant Extended Kalman Filter (IEKF) on the \(SE_n(3)\) group, a breakthrough that provides mathematically exact uncertainty predictions for visual-inertial navigation systems (VINS), cited 16 times. This work eliminates the need for linearization approximations, significantly enhancing filter consistency and robustness in real-world robotics. In parallel, Li pioneered the use of UWB as a stand-alone state estimation solution, demonstrating in his 2023 paper (19 citations) that planar pose estimation can be achieved with high efficiency and without the drift or computational overhead of traditional loop closure detection. By treating UWB as a primary sensor rather than a mere correction tool, his research opens new pathways for low-cost, drift-free localization in GPS-denied environments. Li’s work is essential reading for researchers in autonomous systems, sensor fusion, and geometric control.
Research Focus
Key Achievements
Top Papers
- 1Efficient Planar Pose Estimation via UWB Measurements19 citations · 2023
- 2