Yuma Yamada

Papers

Total Citations

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About

Yuma Yamada is a researcher in robotics and spatial intelligence, with a primary focus on indoor mapping, localization, and 3D perception using LIDAR technology. His most-cited work, "Indoor Map Generation from Multiple LIDAR Point Clouds" (2018, 9 citations), addresses a critical challenge in mobile robotics: constructing accurate indoor maps from multiple 2D LIDAR scans. Yamada’s contribution lies in advancing the iterative closest point (ICP) algorithm—a cornerstone of SLAM (Simultaneous Localization and Mapping)—to better handle the complexities of real-world indoor environments, such as sparse features and sensor noise. By improving point cloud registration and integration, his work enables more reliable autonomous navigation for robots in GPS-denied spaces. Though his citation count is modest, the practical significance of his research is evident in its application to warehouse automation, disaster response, and smart building management. Yamada’s work exemplifies the incremental yet essential engineering innovations that bridge theoretical algorithms and robust real-world deployment, making him a valuable contributor to the field of indoor spatial mapping.