Ying Yu

PLA Information Engineering University, Chinese University of Hong Kong

Papers

Total Citations

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About

Ying Yu is a researcher specializing in robotic perception, sensor fusion, and simultaneous localization and mapping (SLAM), with a particular focus on improving the accuracy and robustness of autonomous systems in dynamic environments. Their most impactful contribution is a high-precision external parameter calibration method for camera and lidar systems, published in 2023 and already garnering 26 citations. This work introduces an auxiliary calibration device with distinctive geometric features, solving a critical prerequisite for reliable sensor fusion in robotics. Yu also developed SIIS-SLAM (2022, 11 citations), a visual SLAM framework based on sequential image instance segmentation that significantly reduces the influence of dynamic objects—a major challenge for real-world autonomous navigation. Earlier work on calibrating multiple Kinect depth sensors for full surface model reconstruction (2016, 6 citations) demonstrated Yu’s foundational expertise in 3D scanning and multi-sensor systems. Collectively, Yu’s research bridges theoretical calibration methods with practical SLAM applications, offering tangible solutions for robots operating in cluttered, unpredictable settings. Their work is essential reading for anyone advancing perception systems in autonomous vehicles, service robotics, or 3D reconstruction.