Sha Lu
Papers
5
Total Citations
188
H-Index
3
About
Sha Lu is a leading researcher in robotics perception, with a primary focus on LiDAR-based global localization and multi-robot collaborative SLAM. Her most impactful work addresses the fundamental challenge of enabling robots to determine their position without GPS, even under extreme viewpoint changes and environmental variations. Her seminal survey, "A Survey on Global LiDAR Localization," has already garnered over 108 citations, establishing it as a key reference in the field. Lu’s major technical contributions include the development of RING++ (72 citations), a novel method that introduces a roto-translation invariant Gram matrix for robust global localization on sparse scan maps, overcoming limitations of prior approaches. She further advanced the field with RING#, which incorporates equivariant learning for enhanced performance. Beyond localization, Lu has contributed to real-time multiview object pose estimation for robotic manipulation and developed a sparse hierarchical LiDAR bundle adjustment method for online collaborative SLAM across multiple robots. Her work is distinguished by its theoretical rigor and practical applicability, directly addressing open problems in autonomous driving and field robotics.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems108 citations · 2024
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