Baowei Lin

Dalian University of Technology, Dalian Neusoft University of Information

Papers

Total Citations

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About

Baowei Lin is a robotics researcher whose work bridges theoretical estimation methods and practical engineering education. His most impactful contribution, the 2019 paper on the "Unscented Particle Filter for Online Total Image Jacobian Matrix Estimation in Robot Visual Servoing," has garnered 23 citations and addresses a core challenge in uncalibrated visual servoing: accurately estimating the image Jacobian matrix in real time. This work enhances how robots use visual feedback to control motion without precise prior calibration, a critical step toward more adaptive and autonomous robotic systems. Beyond algorithm development, Lin is equally committed to shaping the next generation of engineers. His 2016 paper on the "Construction of Robot Practice Teaching System Based on CDIO Model" introduces a project-oriented, four-year educational framework that integrates the CDIO (Conceive-Design-Implement-Operate) engineering paradigm with innovation and entrepreneurship training, specifically tailored for students in Intelligent Science and Technology. By combining rigorous technical research with a pedagogical focus on hands-on, real-world problem-solving, Lin’s career exemplifies a dual dedication to advancing robotic perception and cultivating skilled, creative practitioners in the field.