Bingqing Wan

Southern University of Science and Technology

Papers

Total Citations

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About

Bingqing Wan is a researcher whose work lies at the intersection of robotics, computer vision, and autonomous navigation, with a primary focus on advancing visual SLAM (Simultaneous Localization and Mapping) systems. Her key research areas include direct method visual SLAM, optical flow prediction, and data-driven approaches for improving robotic perception and tracking. Wan’s most notable contribution is her 2022 paper, "Data Driven Optical Flow Prediction for Improving Direct Method Visual SLAM Systems," which addresses a critical limitation in direct and keyframe-based SLAM systems like LDSO. By proposing a data-driven method to predict optical flow for selected pixel points, she enhances frame-to-frame tracking accuracy and motion prediction, overcoming the inherent weaknesses of traditional direct point projection techniques. This work has garnered 3 citations and demonstrates her ability to bridge deep learning with classical robotics pipelines. Wan’s research is particularly impactful for real-world robotic applications, where robust and efficient SLAM is essential for autonomous operation in dynamic environments. Her innovative approach to integrating data-driven insights into established SLAM frameworks marks her as a promising contributor to the field of visual odometry and autonomous systems.