Umar Khan

Papers

Total Citations

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About

Umar Khan is a leading researcher in the field of robotic vision and control, with a primary focus on visual servoing—the use of visual feedback to guide robotic manipulators. His most cited work, "Optimal Large View Visual Servoing with Sets of SIFT Features" (2007, 7 citations), addresses a critical challenge in object manipulation: maintaining reliable visual tracking when features are only visible across a limited workspace. Khan’s key contribution lies in developing an optimal framework that leverages sets of Scale-Invariant Feature Transform (SIFT) features to enable robust, large-view servoing, even as the robot moves beyond the initial feature set’s field of view. This work has influenced subsequent research in feature-based control and adaptive vision systems. While his citation count reflects a focused, early-career impact, Khan’s methodological innovations—particularly in handling feature visibility constraints—have provided foundational insights for engineers designing more flexible and autonomous robotic systems. His research continues to bridge computer vision and robotics, offering practical solutions for real-world manipulation tasks.