Papers

73

Total Citations

2,246

H-Index

23

About

Shoudong Huang is a leading robotics researcher whose work has fundamentally shaped the field of Simultaneous Localization and Mapping (SLAM), with particular focus on algorithm consistency, active exploration, and large-scale mapping. His 2007 paper on convergence and consistency analysis for EKF-based SLAM — garnering over 400 citations — provided rigorous theoretical foundations that the robotics community had long sought, offering formal proofs for nonlinear SLAM problems with range-and-bearing observations. Huang has consistently pushed SLAM into new frontiers, from developing the Sparse Local Submap Joining Filter for large-scale environments to pioneering active SLAM frameworks that integrate model predictive control for efficient robot path planning. His contributions extend into cutting-edge applications, including real-time deformable SLAM for minimally invasive surgery (MIS-SLAM) and 3D LiDAR-based global localization using deep learning for autonomous vehicles. His 2011 SLAM review paper reflects his broad command of the field, while recent work on reliable graphs for SLAM addresses fundamental estimation challenges. With a portfolio accumulating well over 1,000 citations, Huang's research bridges rigorous mathematical theory and practical robotic systems, making him an essential reference point for anyone entering autonomous navigation and mobile robotics research.

Research Focus

Key Achievements

23
H-Index
73
Papers
2,246
Total Citations
31
Avg Citations/Paper
🏆 Most Cited Paper
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
400 citations · 2007
📈 Most Prolific Year: 2007 (6 Papers)
🤝 Key Collaborators: 107
🏛 Institutions: University of Technology Sydney, The University of Sydney, Robotics Research (United States)

Top Papers

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    Reliable Graphs for SLAM
    61 citations · 2019

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 34 days ago