Xiushan Liu

Papers

Total Citations

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About

Xiushan Liu is a researcher at the forefront of computer vision and intelligent robotics, with a primary focus on advancing perception and tracking systems. His work bridges deep learning and visual sensing to solve critical challenges in autonomous navigation and human-robot interaction. Notably, his research on pedestrian tracking algorithms leverages deep learning to enhance foundational tasks like human pose estimation and behavior analysis, which are vital for autonomous driving and intelligent security. In a related vein, Liu has tackled the limitations of low-cost RGB-D sensors in indoor robotics, proposing a novel algorithm to generate high-accuracy 3D point cloud maps for robot navigation—addressing persistent issues of depth noise and poor precision. While his most-cited works, including a 2021 study on deep learning-based tracking and a 2023 paper on visual sensing for navigation, have garnered modest citation counts, they represent targeted contributions to rapidly evolving fields. Liu’s work stands out for its practical orientation, aiming to make advanced vision technologies more accessible and robust for real-world deployment in service robotics and smart environments.