Ryuhei Yamada

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About

Ryuhei Yamada is a researcher whose work sits at the intersection of cloud robotics and autonomous perception systems. His primary research areas include data acquisition frameworks for cloud-based robotic environments, multi-sensor calibration, and 3D mapping for autonomous mobile robots. Yamada’s major contributions center on developing architectures that enable software components to efficiently acquire data from heterogeneous devices and perform context-aware computing using organized knowledge bases. His most cited work, "Data Acquisition Framework for Cloud Robotics" (2019), has garnered 5 citations and addresses the critical challenge of creating software components for cloud robotics environments. In 2024, he advanced the field with "Probability-Based LIDAR–Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map" (3 citations), which improves object classification and 3D model construction for autonomous robots. His 2021 paper further refines data acquisition architecture in cloud robotics. Though his citation counts are modest, Yamada’s work is foundational for researchers tackling the practical integration of sensing and computing in robotic systems, and his calibration methodology represents a notable step toward more reliable autonomous navigation.