Hanjie Liu

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About

Hanjie Liu is a leading researcher in intelligent robotics and autonomous navigation, with a primary focus on visual simultaneous localization and mapping (SLAM) in complex, real-world environments. Liu’s most influential work, “Visual SLAM Based on Dynamic Object Removal” (2019), has garnered 18 citations and addresses a critical limitation of traditional SLAM systems: their assumption of static scenes. By developing a robust framework that detects and removes dynamic objects—such as moving people or vehicles—Liu significantly improves the accuracy and reliability of robot localization in cluttered, unpredictable settings. This contribution is foundational for advancing autonomous systems in applications like service robotics, autonomous driving, and augmented reality. Liu’s research bridges the gap between theoretical SLAM algorithms and practical deployment, demonstrating how dynamic object handling can transform navigation performance. With a growing citation impact, Liu is recognized for pushing the boundaries of robust perception, making robots more adaptable to the messy, moving world they must serve.