Papers
9
Total Citations
57
H-Index
4
About
Xubin Lin is a robotics researcher whose work centers on advancing autonomous navigation, 3D perception, and robotic manipulation in complex, unstructured environments. His primary contributions lie in LiDAR SLAM, point cloud processing, and loop closure detection—critical technologies for long-term robot autonomy. Lin’s most influential work, the Normal Distribution Descriptor (NDD), introduced a novel global descriptor for 3D point cloud loop closure detection, earning 22 citations and establishing a foundation for robust navigation in geometrically sparse scenes. He further addressed the persistent challenge of degeneracy in LiDAR SLAM with his P2d-DO framework, which optimizes localization accuracy under inadequate constraints. Beyond navigation, Lin has explored high-uniformity robotic gluing through a physical-simulation synergy approach, multi-view point cloud registration for robotic measurement, and semantic-geometric mapping for autonomous sprayers. His research also extends to biped climbing robots and appearance-invariant visual localization for long-term deployment. With over 50 cumulative citations and a portfolio spanning from 2019 to 2025, Lin demonstrates a sustained commitment to bridging perception and action in robotics, making his work essential reading for researchers tackling real-world autonomy challenges.
Research Focus
Key Achievements
Top Papers
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- 8Random Walk Network for 3D Point Cloud Classification and Segmentation2 citations · 2019
- 9Appearance-invariant Visual Localization for Long-term Navigation1 citations · 2024