Yemin Hu

Papers

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About

Yemin Hu is a robotics researcher whose work centers on multi-sensor fusion for precise localization and odometry in challenging environments. His key research areas include LiDAR-inertial odometry, visual marker-based navigation, and integrated sensor systems for mobile robots. Hu’s most notable contribution is the development of Marked-LIEO, a visual marker-aided LiDAR/IMU/encoder integrated odometry system designed to overcome the limitations of traditional pose estimation in indoor long corridor environments—a notoriously difficult setting for autonomous navigation. By fusing encoder pre-integration models with IMU data and visual markers, his approach achieves robust, drift-minimized localization where conventional methods fail. This work, published in 2022, has already garnered 10 citations, reflecting its timely relevance to the growing field of indoor mobile robotics. Hu’s research is particularly impactful for applications in warehouse logistics, industrial inspection, and autonomous service robots, where reliable odometry in feature-sparse spaces is critical. His innovative integration of low-cost sensors with advanced filtering techniques marks him as an emerging leader in practical, deployable SLAM solutions.