Qianxing Su

Guangdong Institute of Intelligent Manufacturing

Papers

Total Citations

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About

Qianxing Su is a leading researcher in robotic perception and industrial automation, with a primary focus on computer vision for object pose estimation and bin-picking systems. His most influential work, "Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking" (2020), has garnered 30 citations and addresses a critical bottleneck in modern manufacturing: the need for rapid, accurate pose estimation of randomly oriented objects in cluttered bins. Su’s key contribution lies in developing an adaptive thresholding technique that significantly accelerates pose estimation pipelines without sacrificing precision, enabling real-time robotic grasping in logistics and warehouse automation. This work directly tackles the escalating demand for efficient pick-and-place operations in Industry 4.0 environments. By optimizing the balance between speed and accuracy, Su’s research has practical implications for reducing cycle times and improving reliability in automated systems. His findings are particularly valuable for students and engineers working on vision-guided robotics, offering a scalable solution to one of the most persistent challenges in robotic manipulation. With a growing citation impact, Qianxing Su continues to shape the future of intelligent automation through innovative computer vision methodologies.