Kang Xie
Papers
1
Total Citations
2
H-Index
1
About
Kang Xie is a researcher advancing the frontiers of robotic perception and manipulation, with a focus on hand-eye calibration and 3D object tracking. Their most-cited work, "Online Hand-Eye Calibration with Decoupling by 3D Textureless Object Tracking" (2023), tackles a fundamental challenge in visually guided robotics: accurately estimating the camera-to-robot pose for dynamic object grasping. Unlike conventional methods that rely on 2D fiducial markers and pre-calibration, Xie introduces a decoupling approach using 3D textureless object tracking, enabling real-time, online calibration without specialized markers. This innovation enhances robotic adaptability in unstructured environments, reducing setup complexity and improving precision for tasks like bin picking or assembly. While early in their career, with 2 citations to date, this work signals a promising trajectory in sensorimotor coordination. Xie’s contributions bridge computer vision and robotics, offering a scalable solution for autonomous systems. Their research is particularly valuable for students and engineers seeking robust, markerless calibration techniques, positioning Xie as an emerging voice in practical robotic vision.
Research Focus
Key Achievements
Top Papers
- 1