Xiangcheng Li

Wuhan University of Science and Technology

Papers

Total Citations

H-Index

About

Xiangcheng Li is a researcher whose work lies at the intersection of robotics, computer vision, and simultaneous localization and mapping (SLAM). His primary research focus is on enhancing the robustness and accuracy of SLAM systems, particularly in challenging, dynamic environments where traditional static-scene assumptions fail. Li’s most notable contribution is his work on dynamic object recognition and masking for RGB-D SLAM, a method that systematically identifies and filters out moving entities—such as people or vehicles—to prevent them from corrupting the mapping and localization process. This approach directly addresses a critical bottleneck in deploying autonomous robots in real-world, human-populated spaces. His 2021 paper on the topic has garnered 5 citations, reflecting its relevance to a growing community tackling dynamic SLAM. By improving positioning accuracy in non-static scenes, Li’s research supports the advancement of reliable robotic navigation, with potential applications in service robots, autonomous driving, and augmented reality. His work stands as a practical step toward making SLAM systems truly operational outside controlled laboratory settings.