Papers
22
Total Citations
199
H-Index
8
About
Heoncheol Lee is a robotics researcher whose work centers on autonomous mobile robot systems, with particular expertise in simultaneous localization and mapping (SLAM), multi-robot coordination, and probabilistic estimation methods. His most influential contributions address a fundamental weakness in FastSLAM — the particle depletion problem — through innovative solutions including an adaptive prior boosting technique and the PSO-FastSLAM framework, which integrates particle swarm optimization to maintain long-term accuracy. These works have garnered over 33 and 16 citations respectively, establishing Lee as a notable voice in probabilistic robotics. Expanding beyond single-robot systems, Lee has made sustained contributions to multi-robot map merging, developing spectral and tomographic approaches that enable robots to share spatial knowledge even without prior positional correspondence or inter-robot rendezvous — a practically significant challenge in real-world deployments. His more recent research extends into Antarctic robotic operations, proposing ant colony optimization-based task scheduling tailored to harsh environments, reflecting a commitment to applied, field-relevant robotics. With comparative LIDAR-SLAM analyses rounding out his portfolio, Lee's body of work offers both theoretical rigor and practical guidance, making his research valuable reading for students and practitioners working at the intersection of robot autonomy and collaborative systems.
Research Focus
Key Achievements
Top Papers
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- 5Tomographic Feature-Based Map Merging for Multi-Robot Systems17 citations · 2020
- 6Adaptive prior boosting technique for the efficient sample size in fastSLAM16 citations · 2007
- 7Enhanced-spectrum-based map merging for multi-robot systems12 citations · 2013
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