Pengyu Yin
Papers
2
Total Citations
31
H-Index
2
About
Pengyu Yin is a rising researcher in robotics, specializing in LiDAR-based localization, multi-robot systems, and Simultaneous Localization and Mapping (SLAM). His work addresses critical challenges in autonomous navigation, particularly in enabling robust and efficient pose estimation for robots operating in large-scale, unstructured environments. Yin’s most cited paper, “Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning” (2024, 23 citations), introduces a novel method for one-shot LiDAR localization, allowing a robot to determine its pose from a single point cloud scan—a significant advancement for initialization and relocalization tasks. This work has quickly gained attention for its practical impact on real-world deployment. Additionally, his paper “Multi-Robot Active Graph Exploration with Reduced Pose-SLAM Uncertainty via Submodular Optimization” (2024, 8 citations) tackles the complex problem of coordinating multiple robots to explore environments while maintaining accurate SLAM estimates, using submodular optimization to balance coverage and uncertainty reduction. Yin’s contributions are shaping the future of autonomous exploration and localization, with his research already influencing both academic studies and practical robotic systems.
Research Focus
Key Achievements
Top Papers
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