Dinh Van Nam
Papers
8
Total Citations
182
H-Index
5
About
Dinh Van Nam is a robotics and autonomous systems researcher whose work centers on simultaneous localization and mapping (SLAM), sensor fusion, and state estimation for mobile robots and industrial manipulators. His most influential contribution, a 2021 concise review of solid-state LiDAR-based SLAM (87 citations), has become a widely referenced entry point for researchers exploring autonomous navigation architectures. Building on this foundation, Nam has advanced robust visual-inertial navigation systems, notably developing a stereo visual-inertial odometry framework employing multi-stage outlier removal to maintain accuracy in dynamic indoor environments (34 citations). His more recent work pushes toward practical industrial deployment, integrating multiple 2D LiDAR, visual, and inertial sensors into unified SLAM frameworks for industrial robots. A particularly innovative thread in his research applies Type-2 fuzzy logic and factor graph optimization to achieve adaptive, robust pose estimation under real-world uncertainty (18 citations). Nam has also explored deep learning approaches for state estimation using intrinsic sensors alone, addressing challenging textureless environments where conventional exteroceptive sensors fail. Spanning over 180 cumulative citations, his body of work reflects a sustained commitment to making autonomous robot navigation more reliable, adaptable, and deployment-ready.
Research Focus
Key Achievements
Top Papers
- 1Solid-State LiDAR based-SLAM: A Concise Review and Application87 citations · 2021
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