Papers
2
Total Citations
44
H-Index
2
About
Sungjae Shin is a leading researcher in mobile robotics, specializing in robust localization and state estimation for autonomous systems operating under challenging real-world conditions. His work primarily focuses on sensor fusion, visual-inertial odometry (VIO), and 3D scan matching, addressing critical failures in perception caused by sensor degradation, large odometry uncertainties, and scale ambiguity. In his highly cited 2020 paper, "Normal Distributions Transform is Enough," Shin demonstrated that a 3D NDT-based pose correction framework, fused with an Extended Kalman Filter using IMU and wheel odometry, can maintain accurate localization even under severe odometry drift—a breakthrough for field robots. His 2021 work, "MIR-VIO," introduced a mutual information residual approach to fuse visual-inertial data with UWB ranging, solving the monocular scale problem and achieving robust performance in GPS-denied environments. Collectively, his papers have garnered over 44 citations, reflecting their immediate impact on the robotics community. Shin’s contributions are particularly notable for their practical applicability in drones and mobile robots, where reliable pose estimation is critical for autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2