Wenjuan Huang
Papers
2
Total Citations
6
H-Index
2
About
Wenjuan Huang is a researcher specializing in robotic visual servoing, sensor fusion, and intelligent control systems. Her work focuses on enhancing the accuracy and robustness of uncalibrated binocular stereo visual servoing systems—a critical area for autonomous robotics and computer vision. Huang’s major contributions include the development of a singular value decomposition aided Cubature Kalman filter integrated with neural networks (NNSVDCKF), which significantly improves the estimation of the image Jacobian matrix in dynamic, uncalibrated environments. She further advanced this approach by coupling the SVDCKF with a noise compensator, effectively mitigating the impact of unknown process and measurement noise on system performance. While her most-cited papers have garnered modest citation counts (3 each), they represent foundational work in applying advanced filtering techniques to visual servoing. Notably, her research bridges the gap between theoretical estimation algorithms and practical robotic control, offering a pathway toward more reliable, real-time visual feedback systems. Huang’s work is particularly relevant for researchers exploring adaptive control, neural network-based estimation, and sensor fusion in robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2