Jinyu Miao
Papers
3
Total Citations
51
H-Index
2
About
Jinyu Miao is a robotics researcher whose work focuses on solving fundamental challenges in autonomous navigation, particularly in loop closure detection, visual-inertial odometry, and robotics education. Their most influential contribution is a robust loop closure detection algorithm based on Bag of SuperPoints and graph verification (2019, 39 citations), which helps robots correct accumulated localization errors during long-term exploration—a critical capability for reliable autonomous operation. Miao also developed a scale drift-free visual-inertial odometry system for ground vehicles in highway scenarios (2023), addressing the difficult problem of accurate state estimation when visual features and inertial excitation are sparse. Beyond technical contributions, Miao has advanced robotics education by creating a comprehensive simulation framework using Webots for senior undergraduate robot engineering curricula (2020, 10 citations), enabling students to design and test virtual quadruped robots. This educational work demonstrates Miao's commitment to bridging cutting-edge research with practical training. With a research portfolio spanning robust perception, state estimation, and educational tools, Jinyu Miao is making meaningful contributions to both the technical foundations of autonomous robotics and the training of the next generation of roboticists.
Research Focus
Key Achievements
Top Papers
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