Ju-Feng Wu

About

Ju-Feng Wu is a robotics researcher whose work centers on advancing vision-based control systems, particularly Image-Based Visual Servoing (IBVS). His primary contributions address a fundamental challenge in this field: the accurate estimation of depth information for feature points, which is essential for computing the image Jacobian matrix used in real-time robotic control. In his most-cited work, "Depth estimation of objects with known geometric model for IBVS using an eye-in-hand camera" (2017), Wu proposed a method to derive depth from objects with known geometric models, enabling more precise and stable visual servoing. This research is critical because, for objects without such models, depth retrieval remains notoriously difficult. While his citation count of 5 reflects a focused, emerging impact, Wu’s work lays important groundwork for practical applications in automated manufacturing, inspection, and robotic manipulation. His approach offers a pathway to overcoming one of the key bottlenecks in IBVS, making his contributions valuable for students and researchers exploring vision-guided robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Depth estimation of objects with known geometric model for IBVS using an eye-in-hand camera

   5 citations · 2017

Key Collaborators

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