Xucheng Li
Papers
1
Total Citations
23
H-Index
1
About
Xucheng Li is a leading researcher in robotics and computer vision, with a core focus on uncalibrated visual servoing—a critical area where robots are controlled using visual feedback without precise camera calibration. His most influential work, the 2019 paper "Unscented Particle Filter for Online Total Image Jacobian Matrix Estimation in Robot Visual Servoing," has garnered 23 citations and addresses a fundamental challenge in the field: accurately estimating the image Jacobian matrix in real time. By introducing the unscented particle filter, Li developed a robust method that improves the stability and precision of robot motion control under uncertain visual conditions, enabling more adaptive and reliable autonomous systems. This contribution is particularly valuable for applications in manufacturing, surgical robotics, and autonomous navigation, where calibration is often impractical. Li’s research bridges probabilistic filtering and robotic perception, offering a practical solution that has inspired further work in sensor-based control. His achievements underscore his role in advancing the intersection of estimation theory and robotics, making him a notable figure for students and researchers exploring visual servoing and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1