Junfeng Yuan

Beijing Research Institute of Automation for Machinery Industry (China), East China Jiaotong University

Papers

Total Citations

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About

Junfeng Yuan is a researcher focused on advancing simultaneous localization and mapping (SLAM) for mobile robotics, with particular emphasis on indoor navigation and multirobot coordination. Their work addresses critical challenges in autonomous robot perception, including odometer drift, unreliable sensor data, and map inconsistency in large-scale environments. Yuan’s most cited paper, “Research on SLAM of indoor mobile robot assisted by AR code landmark” (2021, 4 citations), proposes a novel approach to reduce accumulated odometer errors by integrating artificial AR code landmarks, significantly improving positioning accuracy and map fidelity in complex indoor scenes. In earlier foundational work, “Multirobot FastSLAM Algorithm Based on Landmark Consistency Correction” (2014, 2 citations), Yuan introduced an electromagnetism-like mechanism into the resampling process of FastSLAM, enabling multiple robots to collaboratively correct landmark inconsistencies and enhance mapping reliability. Though citation counts are modest, these contributions represent important steps toward robust, scalable SLAM solutions for real-world deployment. Yuan’s research bridges theoretical algorithm design and practical implementation, offering valuable insights for students and engineers working on autonomous navigation, sensor fusion, and multiagent systems.