Ziyu Chen

Chinese Academy of Sciences, University of Science and Technology of China

Papers

Total Citations

H-Index

About

Ziyu Chen is an emerging researcher specializing in simultaneous localization and mapping (SLAM), robotics state estimation, and 3D scene reconstruction. Their work bridges cutting-edge sensor fusion techniques with practical challenges in autonomous mobile systems, positioning them as a contributor to one of robotics' most critical domains. Chen's most notable contribution, "IGE-LIO" (2024), addresses a fundamental limitation in LiDAR-based SLAM systems — the tendency to fail in geometrically degenerated environments. By incorporating intensity gradient information into tightly coupled LiDAR-inertial odometry, Chen's approach significantly enhances localization robustness, earning 21 citations within its first year and signaling strong community uptake. Their subsequent work, "DyGS-SLAM" (2025), tackles the equally challenging problem of dynamic scene reconstruction, merging Gaussian radiance fields with visual SLAM to produce high-quality dense maps even in the presence of moving objects — a capability critical for real-world deployment of autonomous systems. With 31 combined citations across just two publications in a remarkably short timeframe, Chen demonstrates exceptional early-career momentum. Students and researchers working in autonomous navigation, sensor fusion, or neural scene representation will find Chen's contributions both technically rigorous and immediately practically relevant.