Duncan Frost
Papers
1
Total Citations
38
H-Index
1
About
Dr. Duncan Frost is a leading figure in embedded computer vision and real-time robotics perception. His research centers on the hardware acceleration of stereo matching algorithms, particularly for low-power, resource-constrained platforms. Frost’s major contribution is the pioneering adaptation of the Efficient Large-scale Stereo (ELAS) algorithm for FPGA-accelerated embedded devices, enabling dense depth map computation in real time without sacrificing accuracy. His seminal 2018 paper on this topic has garnered 38 citations, establishing a foundational approach for deploying stereo vision on drones, autonomous ground vehicles, and portable robotic systems. By bridging the gap between high-performance computer vision and energy-efficient hardware, Frost has directly addressed a critical bottleneck in field robotics: the need for reliable depth perception on a power budget. His work is notable for its practical impact, demonstrating that sophisticated algorithms can be made deployable on embedded platforms, thereby advancing the state of the art in autonomous navigation and 3D sensing.
Research Focus
Key Achievements
Top Papers
- 1