Seonmo Yang
Papers
1
Total Citations
2
H-Index
1
About
Seonmo Yang is a roboticist whose research centers on Simultaneous Localization and Mapping (SLAM) for resource-constrained platforms, with a particular focus on sparse range sensing and structured environments. His most notable contribution, the SoMaSLAM algorithm, introduces a novel approach to 2D graph SLAM that incorporates a soft Manhattan world model through landmark-landmark constraints. This work addresses a critical challenge in robotics: enabling tiny, low-cost robots that cannot carry heavy or expensive sensors to perform reliable mapping and localization. By leveraging the inherent structural regularities of man-made environments, Yang’s method achieves robust performance even with extremely sparse sensor data. His research has already garnered early citations, signaling its potential impact on the field of minimal-robotics. Yang’s work bridges the gap between theoretical SLAM frameworks and practical deployment on small-scale robots, making autonomous navigation more accessible for applications in search-and-rescue, environmental monitoring, and educational robotics. His innovative use of soft constraints represents a significant step forward in making SLAM feasible for the next generation of compact, affordable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1