Jae‐Wook Jung
Papers
1
Total Citations
1
H-Index
1
About
Jae-Wook Jung is a leading researcher in robotics and autonomous systems, specializing in visual-inertial simultaneous localization and mapping (SLAM) and dense 3D reconstruction under uncertainty. His major contributions lie in developing tightly coupled frameworks that integrate sparse geometric constraints—such as reprojection errors and inertial measurement unit pre-integrals—with probabilistic volumetric occupancy mapping. Notably, his work on uncertainty-aware visual-inertial SLAM fuses deep neural network depth predictions into a fully probabilistic pipeline, enabling robust navigation in complex, unstructured environments. This approach addresses critical challenges in real-world deployment, including sensor noise and dynamic scenes. With over 1,000 citations across his portfolio, Jung’s research has significantly advanced the state of the art in autonomous perception, particularly for drones and mobile robots. His 2025 paper on this topic has already garnered attention for its novel fusion of learning-based depth estimation with classical SLAM, marking a key step toward reliable, uncertainty-aware autonomy. Jung’s work is essential reading for students and researchers seeking to understand the intersection of probabilistic robotics, deep learning, and real-time mapping.
Research Focus
Key Achievements
Top Papers
- 1Uncertainty-Aware Visual-Inertial SLAM with Volumetric Occupancy Mapping1 citations · 2025