Papers
13
Total Citations
118
H-Index
5
About
Tae Gyun Kim is a leading researcher in underwater and mobile robot localization, with a primary focus on probabilistic filtering methods for autonomous navigation. His work centers on comparing and advancing Kalman filter and particle filter approaches, particularly for the challenging domain of underwater robotics where sensor uncertainty and acoustic signal processing are critical. Kim’s most impactful contribution is his 2012 study comparing Kalman and particle filters for underwater vehicle localization (40 citations), which established foundational benchmarks for the field. He further developed particle filter methods using time difference of arrival (TDOA) of acoustic signals (21 citations) and Monte Carlo localization integrating internal and external sensor data (20 citations). His research extends to concurrent mapping and localization (CML) in 3D underwater environments, object tracking using Ultra Wide Band sensors, and synchronous/asynchronous sensor fusion with extended Kalman filters. Through systematic experimental analysis in towing tanks and simulations, Kim has demonstrated how particle filters outperform traditional methods under high uncertainty, making his work essential reading for researchers developing robust localization systems for autonomous underwater vehicles and mobile robots operating in GPS-denied environments.
Research Focus
Key Achievements
Top Papers
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- 4Localization of an Underwater Robot Using Acoustic Signal6 citations · 2012
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- 8Comparison and Analysis of Methods for Localization of a Mobile Robot3 citations · 2013
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