Yukinori Akamine
Papers
2
Total Citations
39
H-Index
2
About
Yukinori Akamine is a leading researcher in robotics and reconfigurable computing, with a primary focus on accelerating real-time object-pose estimation for industrial automation. His most significant contributions center on the iterative-closest-point (ICP) algorithm, a computationally intensive method essential for picking robots to determine object position and orientation. Akamine pioneered the use of field-programmable gate arrays (FPGAs) to create dedicated hardware accelerators for ICP, dramatically improving processing speed. His seminal 2020 paper, "An SoC-FPGA-Based Iterative-Closest-Point Accelerator Enabling Faster Picking Robots" (28 citations), demonstrated a system-on-chip FPGA solution that overcomes the throughput bottleneck in conventional picking robots. Earlier, his 2019 work (11 citations) achieved a 4.8× speedup in object-pose estimation compared to state-of-the-art techniques, validated using Amazon Picking Challenge datasets. By bridging the gap between algorithm efficiency and hardware implementation, Akamine’s research directly addresses the practical demands of high-speed warehouse automation and manufacturing. His work is essential reading for students and engineers interested in FPGA-based acceleration, real-time robotics, and the intersection of computer vision with embedded systems.
Research Focus
Key Achievements
Top Papers
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