Papers
2
Total Citations
51
H-Index
2
About
Feiyi Chen is a leading researcher in robotic perception and sensor fusion, specializing in the extrinsic calibration of multimodal sensing systems. His work addresses a critical challenge in autonomous navigation: achieving robust, high-precision alignment between LiDAR and camera sensors under real-world conditions. Chen’s major contributions include pioneering automatic, targetless calibration methods that eliminate the need for specialized calibration targets. His highly cited paper, “LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With a Globally Optimal Solution” (2023, 31 citations), introduces a globally optimal approach that integrates event cameras—which excel in challenging illumination—with LiDAR, significantly advancing perception in dynamic lighting environments. In “PBACalib: Targetless Extrinsic Calibration for High-Resolution LiDAR-Camera System Based on Plane-Constrained Bundle Adjustment” (2022, 20 citations), he developed a plane-constrained bundle adjustment technique that enhances spatial alignment accuracy for high-resolution systems. With a combined citation impact exceeding 50, Chen’s work is foundational for mobile robotics and autonomous vehicles, enabling more reliable sensor fusion. His innovative, mathematically rigorous solutions continue to shape the future of robust, multimodal perception.
Research Focus
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Top Papers
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