Qiuchen Zhu

Papers

Total Citations

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About

Qiuchen Zhu is a leading researcher at the intersection of robotic vision, structural health monitoring, and medical robotics. Their work is defined by pioneering deep learning and SLAM (Simultaneous Localization and Mapping) techniques for real-world, high-stakes applications. Zhu’s major contributions include the development of a Bidirectional Self-Rectifying Network with Bayesian Modeling (BSNBM) for vision-based crack detection, a robust algorithm for semantic segmentation that enhances automated infrastructure inspection. This work, published in 2022 with 11 citations, addresses the critical need for reliable surface defect identification in built environments. In the medical domain, Zhu introduced BDIS-SLAM, a lightweight, CPU-based dense stereo SLAM system for surgery, achieving 8 citations since 2024. This innovation enables real-time 3D mapping in surgical settings without reliance on expensive GPU hardware, significantly advancing the practicality of computer-assisted interventions. Zhu’s research demonstrates a unique ability to bridge theoretical modeling with deployable systems, impacting both civil engineering and surgical robotics. Their work is essential reading for students and researchers interested in robust perception algorithms for autonomous systems in challenging, safety-critical environments.