Papers

9

Total Citations

108

H-Index

5

About

Massimiliano Nitti is an Italian researcher whose work sits at the intersection of computer vision, robotics, and industrial automation. His primary contributions span 3-D environmental reconstruction, autonomous robot navigation and localization, and vision-based sensing systems for demanding real-world applications. Nitti's most influential work introduced HiPER 3-D, a patented omnidirectional sensor capable of high-precision environmental reconstruction, which has garnered 37 citations and established him as a pioneer in wide-field-of-view 3-D scanning. Building on this foundation, he has made significant strides in indoor robot localization, developing visual odometry systems for omnidirectional automated guided vehicles (AGVs) used in manufacturing environments — work that has attracted 27 citations and seen continued development through 2024. His more recent research integrates deep learning into robotics, notably through the Optical Encoder Neural Network (OE-net), a CNN-based approach to robot pose estimation that reflects his engagement with modern AI-driven methodologies. Beyond robotics, Nitti has extended his expertise into aerospace and advanced materials, contributing to automated aircraft interior inspection (VISTA) and non-destructive quality control for carbon fiber composites. Collectively, his research demonstrates a consistent commitment to translating sophisticated sensing and machine learning techniques into practical industrial solutions.

Research Focus

Key Achievements

5
H-Index
9
Papers
108
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
HiPER 3-D: An Omnidirectional Sensor for High Precision Environmental 3-D Reconstruction
37 citations · 2011
📈 Most Prolific Year: 2024 (2 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Institute of Intelligent Systems for Automation, National Research Council, Tecnologie Avanzate (Italy)

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 16 days ago