Mina Chong

Quanzhou Institute of Equipment Manufacturing Haixi Institute

Papers

Total Citations

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About

Mina Chong’s research lies at the intersection of robotics, computer vision, and intelligent manufacturing, with a focus on enabling machines to perceive, learn from, and interact with their environments. Her work addresses two critical challenges in autonomous systems: robust object recognition for robotic manipulation and efficient environmental mapping for mobile robots. In her most cited paper, “A multi-workpieces recognition algorithm based on shape-SVM learning model” (2018, 5 citations), Chong introduces a novel shape-SVM learning model that allows robots on assembly lines to actively recognize and grasp predetermined workpieces—a key step toward adaptive, learning-based automation. Complementing this, her work “Incremental Mapping Based on Line-Segments Relation for Mobile Robot” (2018, 2 citations) proposes a method for building maps of indoor structured environments using line-segment relations extracted from laser scans, advancing efficient map representation for autonomous navigation. Though early in her career, Chong’s contributions demonstrate a clear trajectory toward integrating machine learning with geometric reasoning for real-world robotic systems. Her work is particularly relevant for researchers in industrial robotics, SLAM, and intelligent automation, offering practical algorithms that bridge perception and action.