Papers
46
Total Citations
2,425
H-Index
22
About
Ayoung Kim is a distinguished robotics researcher whose work spans autonomous navigation, simultaneous localization and mapping (SLAM), and multi-modal perception for robots operating in complex real-world environments. Her contributions have significantly advanced how robots understand and navigate challenging urban and underwater settings. Kim's most impactful work centers on place recognition and LiDAR-based localization. Her Scan Context++ framework (288 citations) introduced robust structural place recognition resilient to rotation and lateral variations, while her "1-Day Learning, 1-Year Localization" approach (136 citations) demonstrated remarkable long-term localization using a single day of training data. Her construction of rich urban datasets (295 citations) has become a foundational resource for the robotics community, capturing the full diversity of real-world urban conditions across multiple sensor modalities. Beyond urban robotics, Kim made notable early contributions to autonomous underwater vehicle navigation for ship hull inspection (251 citations), demonstrating remarkable breadth across domains. Her work on static point cloud mapping (189 citations), active visual SLAM for area coverage, and image enhancement under degraded visibility further reflects her commitment to building perception systems that remain reliable when conditions deteriorate. With multiple papers exceeding 100 citations, Kim's research has become essential reading for anyone working at the intersection of robot perception and autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 5Active visual SLAM for robotic area coverage: Theory and experiment139 citations · 2014
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- 7LiDAR odometry survey: recent advancements and remaining challenges106 citations · 2024
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- 9ViViD++ : Vision for Visibility Dataset77 citations · 2022
- 10Perception-driven navigation: Active visual SLAM for robotic area coverage76 citations · 2013