Yasuki Nagata

Papers

Total Citations

H-Index

About

Yasuki Nagata is a researcher at the forefront of autonomous navigation and robotics for challenging outdoor environments, with a primary focus on forestry and agricultural applications. His work centers on developing robust perception systems that enable mobile robots and heavy forestry vehicles to operate safely in unstructured, dynamic settings. Nagata’s most notable contribution is the integration of LiDAR-SLAM with deep learning architectures like U-Net for real-time forest road surface detection, a critical capability for autonomous log transport and terrain following. This approach addresses the complex variability of forest roads—including changing geometry, surface conditions, and lighting—which traditional methods struggle to handle. His 2021 paper on this topic has garnered 5 citations, establishing a foundation for subsequent work in off-road autonomy. Nagata’s research bridges the gap between advanced computer vision and practical field robotics, with implications for improving safety and efficiency in the forestry industry. His ongoing efforts continue to push the boundaries of what autonomous systems can achieve in natural, unstructured environments.