About

Weitong Wu is a robotics and geospatial researcher specializing in LiDAR-based simultaneous localization and mapping (SLAM), autonomous robot systems, and 3D environmental perception. His work spans the full spectrum of mobile mapping — from affordable ground-based platforms to sophisticated multi-sensor and multi-robot collaborative frameworks. Wu's early contributions focused on practical, low-cost robot laser scanning systems for indoor and outdoor mapping, demonstrating that accessible hardware could achieve reliable 3D reconstruction. He has since advanced significantly more sophisticated systems, including his highly cited DALI-SLAM (2025, 17 citations), which addresses degeneracy-aware LiDAR-inertial odometry with novel distortion correction, and PatchAugNet (2023, 15 citations), a notable contribution to heterogeneous point cloud place recognition in large-scale environments. His panoramic multi-LiDAR inertial odometry work (PMLIO) further tackles the field-of-view limitations inherent in single-sensor configurations. More recently, Wu has pioneered collaborative SLAM research through projects like LuoJia-Explorer and ATCM, enabling aerial-terrestrial heterogeneous robot teams to map environments cooperatively with greater efficiency and accuracy. Collectively accumulating over 50 citations across a rapidly growing body of work, Wu represents an emerging force in intelligent autonomous mapping and spatial robotics research.

Research Focus

Key Achievements

4
H-Index
8
Papers
54
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
DALI-SLAM: Degeneracy-aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization
17 citations · 2025
📈 Most Prolific Year: 2023 (3 Papers)
🤝 Key Collaborators: 31
🏛 Institutions: Hohai University, Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Ministry of Education of the People's Republic of China

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago