Xianghong Zou
Papers
3
Total Citations
53
H-Index
3
About
Xianghong Zou is a leading researcher in robotics perception, specializing in cross-modal data fusion, LiDAR-inertial SLAM, and point cloud place recognition. His work addresses critical challenges in autonomous navigation and 3D scene understanding. Zou’s most influential contribution is **CoFiI2P** (2024, 21 citations), a coarse-to-fine correspondence framework that revolutionizes image-to-point cloud registration by integrating global alignment with local refinement—a breakthrough for robust cross-modality localization. He further advanced SLAM robustness with **DALI-SLAM** (2025, 17 citations), which introduces novel distortion correction and multi-constraint pose graph optimization to handle degeneracy in LiDAR-inertial systems. In large-scale place recognition, Zou’s **PatchAugNet** (2023, 15 citations) pioneers patch-level feature augmentation, enabling reliable heterogeneous point cloud matching in complex street scenes. His work consistently bridges theoretical rigor with practical deployment, achieving high citation impact within just two years. Zou’s contributions are essential for autonomous vehicles and robots operating in challenging, dynamic environments, establishing him as a rising authority in multi-modal perception and spatial intelligence.
Research Focus
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Top Papers
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