Papers
22
Total Citations
385
H-Index
12
About
Tom Drummond is a leading researcher in robotics and computer vision, whose work bridges theoretical elegance with practical, real-time systems. His foundational contributions lie in the application of Lie algebras to visual servoing, where he pioneered novel approaches for using visual feedback to guide robot manipulators with precision and stability. This work, including his highly cited 2000 paper (66 citations), established a rigorous mathematical framework that remains influential in the field. Drummond has also made significant strides in deep learning for 3D scene understanding, developing methods for joint prediction of depth, surface normals, and curvature from single RGB images. His research extends to hardware acceleration, where he led the first FPGA implementation of multilevel ORB feature extraction, dramatically improving computational efficiency for robotic vision. More recently, he has explored human-robot interaction, using augmented reality to visualize robot intent during object handovers, and even applied robotic sensing to improve bionic vision for the visually impaired. With over 300 cumulative citations across his most-cited works, Drummond’s impact is marked by a consistent drive to make robots see, understand, and interact with the world more effectively.
Research Focus
Key Achievements
Top Papers
- 1Application of Lie Algebras to Visual Servoing66 citations · 2000
- 2Visual tracking and control using Lie algebras52 citations · 1999
- 3Visualizing Robot Intent for Object Handovers with Augmented Reality33 citations · 2022
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- 5FPGA acceleration of multilevel ORB feature extraction for computer vision31 citations · 2017
- 6Transformative Reality: Improving bionic vision with robotic sensing23 citations · 2012
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- 9Fast Depth Video Compression for Mobile RGB-D Sensors16 citations · 2015
- 10ORB Feature extraction and matching in hardware16 citations · 2015