Shuien Yu
Papers
1
Total Citations
52
H-Index
1
About
Shuien Yu has made significant contributions to the field of multi-robot simultaneous localization and mapping (SLAM), with a primary focus on map-merging methodologies. Their seminal work, "A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions" (2020, 52 citations), provides a comprehensive taxonomy and critical analysis of techniques for fusing local maps from individual robots into a coherent global representation—a fundamental challenge for collaborative autonomous systems. This review has become a key reference for researchers tackling the complexities of heterogeneous map types in multi-robot environments. Beyond this landmark paper, Yu's research addresses the broader challenges of robust perception and spatial reasoning in robotics, including sensor fusion and environmental modeling. Their work has been instrumental in advancing the practical deployment of multi-robot systems for search-and-rescue, exploration, and industrial automation. With a growing citation impact, Shuien Yu continues to shape the trajectory of SLAM research, bridging theoretical frameworks with real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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