Jingyuan Tan

Papers

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About

Jingyuan Tan is a researcher specializing in robotics, sensor fusion, and autonomous navigation, with a particular focus on indoor mobile robot localization. Their most cited work, "Indoor Wheeled Robot Positioning Algorithm Based on Extended Kalman Filter" (2019), addresses a critical challenge in robotics: the unreliability of single-sensor data for accurate positioning. By developing an enhanced Extended Kalman Filter (EKF) algorithm, Tan demonstrated how fusing multiple sensor inputs can significantly improve the precision and robustness of wheeled robot localization in indoor environments. This contribution is foundational for applications ranging from industrial automation to service robotics, where reliable navigation is essential. Although the paper has garnered 2 citations, its practical relevance underscores Tan’s focus on solving real-world engineering problems. Their work contributes to the broader field of mobile robotics, offering a method that balances computational efficiency with positioning accuracy. Tan’s research is particularly valuable for students and engineers seeking to implement robust localization systems in cost-sensitive or space-constrained indoor settings, highlighting the enduring importance of sensor fusion in autonomous systems.