Yujie Cui

Papers

Total Citations

H-Index

About

Yujie Cui is a rising researcher in robotics and embodied intelligence, whose work centers on advancing perception and mapping for autonomous mobile systems. His primary research areas include simultaneous localization and mapping (SLAM), LiDAR data understanding, and self-supervised learning for robotic perception. Cui’s major contribution lies in developing novel frameworks that enable 2-D LiDAR point clouds to be semantically differentiated—a critical challenge that has traditionally limited SLAM performance in unstructured environments. His most-cited paper, “Self-Supervised Point Cloud Importance Awareness Network for 2-D LiDAR SLAM” (2025), introduces an innovative approach that leverages self-supervision to identify and prioritize salient features in sparse LiDAR data, significantly enhancing mapping accuracy and robustness without requiring labeled datasets. Although early in his career, this work has already garnered attention (2 citations), reflecting its timely relevance to the growing demand for efficient, data-driven SLAM solutions. Cui’s research bridges the gap between low-cost 2-D LiDAR sensors and high-level scene understanding, offering a practical pathway toward more capable and autonomous mobile robots. His contributions are particularly notable for addressing a fundamental bottleneck in embodied AI, positioning him as a promising voice in the next generation of robotic perception researchers.