Keisuke Sugiura
Papers
6
Total Citations
76
H-Index
5
About
Keisuke Sugiura is a researcher specializing in embedded hardware acceleration, autonomous robotics, and edge computing, with a particular focus on implementing computationally intensive algorithms on Field-Programmable Gate Arrays (FPGAs). His work sits at the intersection of robotics and hardware engineering, addressing one of the field's most pressing challenges: enabling resource-constrained mobile robots to perform sophisticated real-time tasks without relying on power-hungry, high-cost processors. Sugiura has made significant contributions to LiDAR-based Simultaneous Localization and Mapping (SLAM), developing efficient FPGA implementations that accelerate scan matching and environment modeling for applications ranging from household cleaning robots to industrial navigation systems. His most cited work, "A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA" (2022, 30 citations), demonstrates his ability to deliver practical, scalable solutions accessible to low-end hardware platforms. He has further extended his expertise into deep learning-based path planning, proposing P3Net, a PointNet-based architecture optimized for FPGA deployment that enables real-time 2D and 3D navigation. Collectively accumulating over 75 citations, Sugiura's research offers robotics developers a compelling pathway toward achieving autonomy on affordable, energy-efficient hardware.
Research Focus
Key Achievements
Top Papers
- 1A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA30 citations · 2022
- 2An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm19 citations · 2021
- 3An Integrated FPGA Accelerator for Deep Learning-Based 2D/3D Path Planning11 citations · 2024
- 4A unified accelerator design for LiDAR SLAM algorithms for low-end FPGAs7 citations · 2021
- 5P3Net: PointNet-based Path Planning on FPGA5 citations · 2022
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