Byeongyong Hyeon
Papers
1
Total Citations
2
H-Index
1
About
Byeongyong Hyeon is a researcher whose work centers on advancing robotic perception and navigation, with a primary focus on Simultaneous Localization and Mapping (SLAM). His key contributions lie in improving the robustness and accuracy of SLAM algorithms, particularly by addressing the critical problem of particle depletion in FastSLAM. In his most cited work, "An Improved FastSLAM Algorithm using Fitness Sharing Technique" (2012), Hyeon introduced a novel method to maintain particle diversity during the resampling phase. By applying a fitness sharing technique, his algorithm prevents the loss of valuable particle hypotheses, leading to more reliable map building and localization. This work has earned 2 citations and represents a meaningful step forward in making SLAM more resilient for real-world robotic applications. Hyeon’s research is especially relevant for students and engineers working on autonomous navigation, as it tackles a fundamental challenge in probabilistic robotics. His approach demonstrates a clever integration of evolutionary computation concepts with traditional SLAM frameworks, offering a practical solution that enhances the performance of mobile robots in complex environments.
Research Focus
Key Achievements
Top Papers
- 1An Improved FastSLAM Algorithm using Fitness Sharing Technique2 citations · 2012