Shangkun Zhong

City University of Hong Kong, Harbin Institute of Technology

Papers

Total Citations

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About

Shangkun Zhong is a leading researcher in visual-inertial odometry (VIO) and autonomous navigation for micro aerial vehicles (MAVs). His work focuses on developing efficient, lightweight estimation algorithms that enable small drones to perceive their environment and estimate their motion with minimal computational resources. Zhong's major contributions include pioneering direct visual-inertial methods that bypass traditional feature extraction by using raw intensity measurements from images, dramatically improving speed and robustness in texture-poor or dynamic scenes. His 2020 paper on an iterated extended Kalman filter (EKF) for direct VIO, which leverages a single plane primitive and homographic relations, has garnered 10 citations for its novel approach to real-time ego-motion estimation. In 2021, he advanced this work with a one-step visual-inertial estimator using photometric feedback, achieving robust altitude and motion estimation for small aerial robots. Additionally, his 2019 work on a scalable fiducial marker-based motion capture system (4 citations) demonstrates his versatility in creating practical, deployable solutions for indoor tracking. Zhong's research is distinguished by its emphasis on computational efficiency and direct sensor fusion, making his algorithms ideal for resource-constrained platforms like MAVs.