Papers
2
Total Citations
38
H-Index
2
About
Y. Kay is a researcher in robotics and computer vision, with a primary focus on 3-D motion estimation and stereo vision systems for robotic manipulation. Their work centers on developing robust, accurate methods for determining the position and orientation of moving objects—a critical challenge for robot-environment interaction. Kay’s most influential contribution, “An accurate estimation of 3-D position and orientation of a moving object for robot stereo vision: Kalman filter approach” (2002), introduced a novel Kalman filter framework that explicitly accounts for camera position uncertainty, achieving high-precision pose estimation. This paper has garnered 33 citations, reflecting its impact on the field. A related study, “A robust 3-D motion estimation with stereo cameras on a robot manipulator” (2002), further advanced the state of the art by addressing pose estimation from cameras mounted on a moving robot arm, emphasizing robustness in dynamic conditions. Kay’s work is notable for its practical integration of uncertainty modeling into real-time stereo vision systems, laying groundwork for applications in autonomous robotics and industrial automation. Their research remains a valuable reference for engineers developing vision-guided robotic systems.
Research Focus
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Top Papers
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