Linlin Ou
Papers
9
Total Citations
92
H-Index
6
About
Linlin Ou is a robotics researcher whose work spans human-robot interaction, autonomous navigation, and intelligent manufacturing. With a strong focus on bridging perception and action, Ou has made significant contributions to enabling robots to understand and respond to human behavior in real time. Their most widely cited work — on point cloud modeling for spray painting robots (21 citations) and human-robot collaborative interaction through pose estimation (20 citations) — demonstrates a dual commitment to industrial automation and intuitive human-robot communication. Ou's development of multi-view 3D human pose estimation systems has advanced the field of safe, flexible human-robot collaboration, while their Dynamic Window with Virtual Goal (DW-VG) obstacle avoidance method reflects deep expertise in reactive navigation within dynamic environments. Work on linear temporal logic-based path planning and multi-robot collaborative transportation further highlights a rigorous theoretical foundation applied to complex real-world tasks. More recently, Ou has pushed into articulated object pose estimation for embodied intelligence applications. Collectively accumulating over 90 citations, Ou's research portfolio reflects a productive and evolving career at the intersection of computer vision, motion planning, and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Multi-View Human Pose Estimation in Human-Robot Interaction14 citations · 2020
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- 6Research on multi-robot collaborative transportation control system8 citations · 2016
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