Hyeyeon Chang

Korea Institute of Science and Technology

Papers

Total Citations

H-Index

About

Hyeyeon Chang is a robotics researcher whose work centers on mobile robot localization and autonomous navigation, with a particular focus on probabilistic and sensor fusion methodologies. Active in the mid-2000s, Chang made meaningful contributions to the challenge of reliable position estimation for service robots operating in indoor environments — a problem of considerable practical importance as autonomous systems began entering real-world settings. Chang's most recognized work introduces a Monte Carlo localization framework that integrates laser scanner data with indoor GPS systems, enabling robots to estimate their positions with greater robustness even when individual sensor readings are unreliable or noisy. By combining odometry, range imaging, and indoor GPS into a unified probabilistic model, this research addressed a core limitation of single-sensor approaches — susceptibility to environmental interference and measurement error. Published across several 2006 venues, including the proceedings of the prestigious 23rd ISARC conference, Chang's papers have collectively accumulated citations that reflect their value as foundational references in service robotics localization. For students exploring sensor fusion, probabilistic robotics, or indoor navigation systems, Chang's work offers an accessible yet technically rigorous entry point into the challenges of making mobile robots reliably aware of their position in complex, real-world environments.