Papers
7
Total Citations
82
H-Index
5
About
Kyung Min Han is a robotics researcher whose work spans autonomous navigation, underwater perception, and real-time 3D mapping. His contributions address fundamental challenges in enabling robots to operate in unstructured and visually degraded environments. Han’s research is particularly notable for its focus on practical, computationally efficient solutions—from collision-free path planning (2007, 17 citations) to geolocating targets from airborne video without terrain data (2010, 19 citations). He has made significant strides in underwater robotics, developing shape-context-based object recognition and tracking systems that overcome poor lighting and turbidity (2011, 18 citations), as well as novel landmarks for autonomous docking (2012, 2 citations). More recently, Han has advanced autonomous exploration with his Autoexplorer system (2022, 10 citations), which uses fast frontier-region detection and parallel path planning, and OctoMap-RT (2023, 14 citations), a GPU-accelerated probabilistic volumetric mapping method that dramatically speeds up 3D environment modeling. His latest work, Neuro-Explorer (2024, 2 citations), introduces learning-based frontier region identification for scalable exploration. With over 80 cumulative citations, Han’s research consistently pushes the boundaries of robotic autonomy in challenging real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Geolocation of Multiple Targets from Airborne Video Without Terrain Data19 citations · 2010
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- 3Collision free path planning algorithms for robot navigation problem17 citations · 2007
- 4OctoMap-RT: Fast Probabilistic Volumetric Mapping Using Ray-Tracing GPUs14 citations · 2023
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