Guangcheng Chen

Guangdong University of Technology, Southern University of Science and Technology

Papers

Total Citations

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About

Guangcheng Chen is a robotics researcher whose work focuses on advancing Simultaneous Localization and Mapping (SLAM) for autonomous mobile robots, particularly in dynamic and computationally constrained environments. His key research areas include 2D LiDAR SLAM, Visual SLAM (VSLAM), and cloud-edge collaborative robotics. Chen’s major contributions address two critical challenges in the field: handling dynamic indoor environments and offloading computational demands. In his highly cited 2021 paper, "Mapping While Following," he pioneered a method integrating a person tracker with 2D LiDAR SLAM, enabling robots to map dynamic indoor spaces while autonomously following a human operator—a significant departure from traditional joystick-controlled mapping in static settings. This work has garnered 10 citations for its practical relevance to service robotics. More recently, his 2023 study, "Cloud Learning-Based Meets Edge Model-Based," proposed a hybrid architecture where robots leverage cloud-based learning for complex VSLAM tasks without needing to build all submaps locally. This innovation, with 6 citations, addresses the computational bottleneck of learning-based methods in mobile robotics. Chen’s research is notable for bridging theoretical SLAM algorithms with real-world deployment challenges, making him a promising voice in the evolution of intelligent, autonomous navigation systems.