Papers
2
Total Citations
6
H-Index
2
About
Seigo Ito is a leading researcher in autonomous robotics, with a focus on localization, perception, and sim-to-real transfer for industrial automation. His work on small imaging LIDAR systems, notably the SPAD LIDAR using single-photon avalanche diodes, has advanced the field of 3D localization for autonomous robots. His 2017 paper on SPAD DCNN, which integrates deep convolutional neural networks with small LIDAR for robust localization, has garnered 4 citations and laid groundwork for efficient sensor fusion. More recently, Ito has pioneered zero-shot sim-to-real learning for forklift automation, as demonstrated in his 2025 paper on a visual-based forklift learning system that operates without real-world training data, earning 2 citations. This work addresses critical safety and data scarcity challenges in industrial robotics. Ito’s contributions are notable for bridging the gap between simulation and real-world deployment, particularly for counterbalance forklifts in diverse industrial settings. His research is impactful for students and engineers seeking to develop autonomous systems that are both safe and data-efficient, pushing the boundaries of practical robotics.
Research Focus
Key Achievements
Top Papers
- 1SPAD DCNN: Localization with small imaging LIDAR and DCNN4 citations · 2017
- 2