Jingxin Lin
Papers
2
Total Citations
17
H-Index
2
About
Jingxin Lin is a researcher at the forefront of robotic vision and human-robot interaction, specializing in sensor calibration and intelligent perception. Her work addresses critical challenges in enabling robots to accurately perceive and interact with their environments. Lin’s most cited paper, “Hand-eye calibration method for a line structured light robot vision system based on a single planar constraint” (2024, 12 citations), introduces a novel, simplified calibration technique that significantly enhances the precision of 3D vision-guided robots. In her equally notable work, “Prior Information-Assisted Neural Network for Point Cloud Segmentation in Human-Robot Interaction Scenarios” (2024, 5 citations), she pioneers a deep learning approach that leverages robot joint angles as prior knowledge to dramatically improve point cloud segmentation—a key capability for safe and intuitive human-robot collaboration. With a total of 17 citations on just two recent papers, Lin’s contributions are rapidly gaining recognition for their practical impact, offering efficient solutions that bridge the gap between advanced vision algorithms and real-world robotic systems. Her research is essential reading for engineers and scientists developing next-generation interactive robots.
Research Focus
Key Achievements
Top Papers
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- 2