Papers
12
Total Citations
271
H-Index
8
About
Hyungtae Lim is a robotics researcher whose work spans simultaneous localization and mapping (SLAM), autonomous navigation, and sensor fusion — areas critical to the development of intelligent mobile systems. His most recognized contribution, *DynaVINS* (2022), addresses a fundamental challenge in visual-inertial SLAM by enabling robust pose estimation in dynamic real-world environments, earning over 120 citations and establishing him as a notable voice in robust localization research. Complementing this, his *ERASOR2* framework (2023) advances static map construction by intelligently filtering dynamic objects during 3D mapping, directly improving reliability for robot navigation pipelines. Lim has also made meaningful strides in LiDAR-camera cross-modal place recognition, 3D scan matching under odometry uncertainty, and deep learning-based indoor localization through his *RONet* architecture, which leverages stacked bidirectional LSTMs for range-only positioning. His 2024 survey on ground segmentation and traversability estimation further reflects his breadth across terrestrial robotics. With contributions spanning perception, mapping, and localization — and a growing citation record across multiple publication venues — Lim represents an emerging and versatile force in the autonomous robotics research community.
Research Focus
Key Achievements
Top Papers
- 1<i>DynaVINS:</i> A Visual-Inertial SLAM for Dynamic Environments123 citations · 2022
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- 4(LC): LiDAR-Camera Loop Constraints for Cross-Modal Place Recognition23 citations · 2023
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- 8DynaVINS: A Visual-Inertial SLAM for Dynamic Environments9 citations · 2022
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