Eunseong Jang
Papers
1
Total Citations
5
H-Index
1
About
Eunseong Jang is a rising researcher in robotics and autonomous systems, with a primary focus on simultaneous localization and mapping (SLAM) in dynamic environments. His work addresses a critical challenge in real-world robotics: how to effectively handle moving objects that traditionally degrade map accuracy. Jang’s most cited paper, "A New Multimodal Map Building Method Using Multiple Object Tracking and Gaussian Process Regression" (2024), introduces a novel framework that goes beyond simply ignoring dynamic features. Instead, it actively tracks multiple objects and uses Gaussian Process Regression to build more robust, context-aware maps. This approach represents a significant shift from conventional SLAM methods, which often discard valuable dynamic object information. While his citation count is still growing—with 5 citations on his top paper—the work has already been recognized for its potential to improve autonomous navigation in crowded, unpredictable settings. Jang’s contributions are paving the way for smarter, more adaptive robots that can understand and coexist with dynamic surroundings, making his research highly relevant for students and engineers working on next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1