Sangkyung Sung
Papers
5
Total Citations
64
H-Index
4
About
Sangkyung Sung is a leading researcher in autonomous navigation, sensor fusion, and inertial systems, with a career dedicated to enhancing the precision and reliability of mobile robot and vehicle positioning. His major contributions lie in developing robust navigation algorithms that integrate diverse sensors—including magnetometers, vision-based SLAM, and deep learning-driven lidar odometry—to overcome the limitations of individual systems. For instance, his highly cited 2015 work (31 citations) introduced a method to improve inertial navigation by leveraging magnetic measurements and vehicle dynamic constraints, significantly boosting accuracy in ground vehicles. His pioneering 2008 paper (17 citations) advanced vision-based SLAM for mobile robots in GPS-denied environments, while his 2022 study (10 citations) proposed a novel pose estimation system using concurrent AC magnetic fields, offering a new paradigm for dynamic object tracking. More recently, his 2020 research (2 citations) explored deep learning-integrated lidar odometry, pushing the boundaries of sensor fusion. With a portfolio of work that consistently bridges theory and practical implementation, Sung’s research has become essential reading for engineers and researchers advancing autonomous systems, from indoor robots to autonomous vehicles.
Research Focus
Key Achievements
Top Papers
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