Linyan Cui

Papers

Total Citations

206

H-Index

About

Linyan Cui is a researcher specializing in computer vision and robotics, with a particular focus on Simultaneous Localization and Mapping (SLAM) systems. Their work centers on one of the most pressing challenges in the field: enabling robots and autonomous systems to accurately navigate and map real-world environments that contain moving objects — a problem that has long undermined traditional SLAM frameworks built on static-world assumptions. Cui's most impactful contribution, SOF-SLAM: A Semantic Visual SLAM for Dynamic Environments (2019), has accumulated 159 citations, establishing them as a meaningful voice in dynamic SLAM research. By integrating semantic understanding into the SLAM pipeline, this work provided a practical pathway for handling the unpredictable, cluttered environments robots encounter in everyday deployment. Their follow-up work, SDF-SLAM (2020), further refined this approach through semantic depth filtering, earning 42 citations and reinforcing the direction of their research agenda. Additionally, their Direct-ORB-SLAM paper explored hybrid direct-feature methods to improve pose estimation and mapping density beyond conventional feature-based approaches. Cui's body of work reflects a consistent drive to bridge the gap between idealized SLAM assumptions and the messy complexity of real-world robotics applications, making their research particularly valuable for practitioners building robust autonomous systems.