Papers
5
Total Citations
103
H-Index
4
About
Zhengke Qin is a robotics and computer vision researcher whose work sits at the intersection of industrial automation, sensor fusion, and machine learning. His most significant contribution lies in the development of precision robotic assembly systems for large-scale objects, combining multi-camera guidance with laser distance sensing to achieve high-accuracy alignment in manufacturing environments — a paper that has garnered 72 citations and stands as a landmark in industrial robotics. Building on this foundation, Qin has advanced the field of robot vision calibration, introducing a self-calibration framework capable of simultaneously estimating camera intrinsic parameters and hand-eye geometry using only two feature points, earning 18 citations for its elegant practicality. His intellectual curiosity extends into deep learning applications, where he surveyed emerging methods for object pose recovery — a critical capability for robotic manipulation and augmented reality. Qin has also contributed to facial recognition for service robots, proposing an expression-aware enhancement to local binary pattern histograms. Across his body of work, Qin demonstrates a consistent drive to bridge theoretical computer vision with real-world robotic applications, making him a meaningful contributor to intelligent manufacturing and autonomous systems research.
Research Focus
Key Achievements
Top Papers
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