Jin Woo Song
Papers
6
Total Citations
54
H-Index
3
About
Jin Woo Song is a rising leader in autonomous robot navigation, specializing in state estimation, sensor fusion, and localization for micro aerial vehicles (MAVs) and mobile robots. His work centers on enabling real-time, onboard autonomy in GPS-denied and unknown environments—critical for search-and-rescue and industrial inspection tasks. Song’s most cited paper (2020, 22 citations) introduces a stereo-camera-based system for autonomous state estimation and mapping, allowing MAVs to navigate without external infrastructure. He has pioneered innovative sensor fusion techniques, including sequential batch fusion for magnetic anomaly navigation (2023, 19 citations) and radar odometry with accelerometer aiding for dead reckoning (2024, 7 citations). His recent theoretical work on error-state Kalman filtering with linearized state constraints (2025) addresses a key gap in robotics and aerospace localization. Song’s research consistently pushes the boundaries of low-cost, robust navigation, demonstrating impact through magnetic-map-matching and dead-reckoning fusion methods that operate reliably in magnetically distorted indoor environments. With a growing citation record and a focus on practical, deployable solutions, Song is shaping the future of autonomous navigation for resource-constrained robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Error-State Kalman Filtering with Linearized State Constraints3 citations · 2025
- 5
- 6