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About

Guanglei Huo is a leading researcher in mobile robotics, specializing in semantic navigation, human-robot interaction, and intelligent mapping for indoor environments. His work bridges the gap between raw sensor data and high-level scene understanding, enabling robots to perceive and act within complex, human-centric spaces. Huo’s most influential contribution is his 2018 paper on “Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots,” which has garnered 31 citations. In this work, he proposed a three-layer perception framework using transfer learning to enhance a robot’s ability to recognize places, rotation regions, and sides—a foundational step for robust semantic navigation. He further advanced the field with a novel fusion method for indoor mapping (2014), employing Kalman Filtering to reduce cumulative odometry errors, and a dynamic graph-based approach for semantic region estimation (2016), specifically designed to support long-term operation of assistive robots for the elderly. Most recently, Huo has explored spatio-temporal semantic features for real-time human-robot interaction (2023), aiming to infer human behavioral intentions. His research consistently targets the critical challenge of making robots more perceptive, stable, and socially aware in real-world indoor settings.