Papers
24
Total Citations
358
H-Index
10
About
Jongdae Jung is a robotics and autonomous systems researcher whose work spans indoor localization, simultaneous localization and mapping (SLAM), and autonomous vehicle navigation across diverse environments. His research has made significant contributions to solving the SLAM problem through innovative sensor fusion approaches, most notably leveraging ambient magnetic fields and radio frequency signals to enable robust indoor robot positioning — a landmark 2014 study that has garnered 78 citations. Jung has systematically advanced SLAM methodology through observability analysis using Fisher information matrices, sequence-based magnetic field matching, and fuzzy-logic-assisted probabilistic frameworks for mobile robot localization. Beyond indoor robotics, his work extends to challenging marine and underwater domains, including the development of autonomous surface vehicles (ASVs) and AUV navigation using vision-based SLAM with artificial landmarks. His 2020 study on autonomous surface vehicle performance evaluation has attracted 39 citations, reflecting strong community interest in maritime autonomy. More recently, Jung has pushed toward multi-robot cooperative systems, demonstrating coordinated ASV navigation in real marine field trials. His contributions also touch civil infrastructure monitoring through robotic structural health inspection systems. With a cumulative citation profile exceeding 300 citations, Jung represents a versatile and impactful voice in autonomous robotics research.
Research Focus
Key Achievements
Top Papers
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- 4DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis36 citations · 2014
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- 7AUV SLAM using forward/downward looking cameras and artificial landmarks20 citations · 2017
- 8Indoor localization using particle filter and map-based NLOS ranging model17 citations · 2011
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