Papers
8
Total Citations
281
H-Index
8
About
Seungwon Song is a robotics researcher whose work sits at the intersection of simultaneous localization and mapping (SLAM), autonomous navigation, and field robotics. His most influential contribution, *DynaVINS: A Visual-Inertial SLAM for Dynamic Environments* (2022), has garnered over 120 citations and directly addresses one of the field's most persistent challenges — maintaining robust pose estimation in real-world settings populated by moving objects. This work has become a key reference for researchers developing SLAM systems for service robots, drones, and autonomous vehicles. Beyond SLAM, Song has made meaningful contributions to autonomous infrastructure inspection, proposing frameworks for drone-based bridge inspection and developing a novel wall-climbing drone with a rotary arm capable of navigating irregularly shaped surfaces — work with clear implications for structural health monitoring in hazardous environments. His *GP-ICP* method advances point cloud registration for ground vehicles, earning 37 citations, while his *LC²* framework tackles cross-modal place recognition by fusing LiDAR and camera data for reliable re-localization. Across his body of work, Song consistently bridges theoretical algorithm development with real-world robotic deployment, making his research particularly valuable for engineers and students working on practical autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1<i>DynaVINS:</i> A Visual-Inertial SLAM for Dynamic Environments123 citations · 2022
- 2Toward Autonomous Bridge Inspection: A framework and experimental results53 citations · 2019
- 3GP-ICP: Ground Plane ICP for Mobile Robots37 citations · 2019
- 4(LC): LiDAR-Camera Loop Constraints for Cross-Modal Place Recognition23 citations · 2023
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- 7DynaVINS: A Visual-Inertial SLAM for Dynamic Environments9 citations · 2022
- 8Concept Design for Mole-Like Excavate Robot and Its Localization Method8 citations · 2019