Keisuke Yamamoto
Papers
2
Total Citations
39
H-Index
2
About
Keisuke Yamamoto is a leading researcher in robotics and reconfigurable computing, whose work bridges the gap between high-speed hardware acceleration and practical industrial automation. His primary research areas include FPGA-based accelerators, object pose estimation, and real-time robotic manipulation. Yamamoto’s most significant contribution is the development of a field-programmable gate array (FPGA)-based accelerator for the iterative closest point (ICP) algorithm, a computationally intensive method for object pose estimation in picking robots. His 2020 paper, “An SoC-FPGA-Based Iterative-Closest-Point Accelerator Enabling Faster Picking Robots,” has garnered 28 citations and demonstrates a breakthrough in overcoming the low throughput of conventional picking systems. Earlier, his 2019 work achieved a 4.8-times faster pose estimation than state-of-the-art techniques, validated using Amazon Picking Challenge datasets. By offloading the ICP algorithm to specialized hardware, Yamamoto has enabled robots to perceive and grasp objects with unprecedented speed and accuracy. His research directly addresses critical bottlenecks in warehouse automation and manufacturing, making him a key figure in the evolution of intelligent, high-performance robotic systems.
Research Focus
Key Achievements
Top Papers
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