Yiting Liu
Papers
1
Total Citations
6
H-Index
1
About
Yiting Liu is a researcher advancing precision in agricultural robotics, with a primary focus on hand–eye calibration for fruit-picking automation. Her most cited work, “Research on Hand–Eye Calibration Accuracy Improvement Method Based on Iterative Closest Point Algorithm” (2023), addresses a critical bottleneck in robotic harvesting: the accuracy of the spatial relationship between a robot’s vision system and its manipulator. By integrating the Iterative Closest Point (ICP) algorithm into the calibration pipeline, Liu significantly improves upon traditional linear and nonlinear methods, enhancing operational precision for apple-picking robots. This contribution directly impacts the efficiency and reliability of automated harvesting systems, a key challenge in precision agriculture. With 6 citations to date, her work is gaining traction among researchers in robotics and agricultural engineering. Liu’s research underscores the importance of sensor fusion and algorithmic refinement in real-world robotic applications, positioning her as an emerging voice in the intersection of computer vision, calibration theory, and agricultural automation. Her efforts help bridge the gap between laboratory accuracy and field-ready performance.
Research Focus
Key Achievements
Top Papers
- 1