Xijiang Chen

Papers

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About

Xijiang Chen is a researcher at the forefront of intelligent robotics and computer vision, with a primary focus on enhancing environmental perception and safety systems. His most impactful work, "A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing" (2022, 40 citations), addresses critical limitations in traditional fire detection by developing a deep learning-based vision system. This model overcomes the vulnerability of conventional temperature and smoke detectors to environmental interference, enabling robots to identify fires with greater accuracy and reliability—a significant contribution to autonomous safety and disaster response. More recently, Chen has tackled the challenge of dynamic environments in robotics. His 2025 paper, "Dynamic SLAM Dense Point Cloud Map by Fusion of Semantic Information and Bayesian Moving Probability," introduces a novel approach to Simultaneous Localization and Mapping (SLAM) that moves beyond the restrictive static-world assumption. By integrating semantic understanding with probabilistic modeling of object motion, his work promises to dramatically improve localization and mapping consistency for robots operating in real-world, changing spaces. This dual focus on robust sensing and adaptive mapping positions Chen as a key innovator in making intelligent robots more capable and resilient in complex, unstructured environments.