Xiaohan Chen

Beihang University, Singapore University of Technology and Design

Papers

Total Citations

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About

Xiaohan Chen is a robotics researcher whose work centers on perception, localization, and state estimation for autonomous systems. Chen’s most influential contribution addresses a fundamental challenge in mobile robotics: reliable indoor localization without GPS. In their highly cited 2014 paper (51 citations), Chen introduced an innovative visual system that uses lampshade corners as natural landmarks for robot positioning. This work, encompassing camera calibration, feature extraction, and experimental validation, provided a practical, low-cost solution for indoor navigation—a problem critical to service robots and warehouse automation. Chen further advanced robotic manipulation with a 2015 study on pose estimation for robotic end-effectors under low-speed motion. By designing an Extended Kalman Filter (EKF) that fuses inertial measurement unit (IMU) data with SE(3) measurements, Chen tackled the challenge of accurately tracking position and orientation when accelerations are small relative to gravity. This work is particularly valuable for precision tasks in manufacturing and surgical robotics. Through these contributions, Chen has helped bridge the gap between theoretical estimation algorithms and real-world robotic applications, establishing a foundation for more robust and autonomous systems in GPS-denied environments.