Daniele Evangelista
Papers
8
Total Citations
45
H-Index
5
About
Daniele Evangelista is a robotics and computer vision researcher whose work spans industrial inspection automation, sensor calibration, autonomous navigation, and human-robot collaboration. His research addresses the practical challenges of deploying intelligent robotic systems in demanding real-world environments, particularly aerospace and manufacturing industries. Evangelista's most recognized contributions include the SPIRIT framework (2020), which revolutionizes how complex industrial inspection tasks are configured rather than manually programmed, and a series of influential hand-eye calibration methods that push beyond traditional mathematical constraints. His 2022 unified iterative calibration approach and a subsequent graph-based optimization framework for multi-camera setups (2023) demonstrate a sustained commitment to improving robotic perception accuracy — work that has collectively garnered over 15 citations. In autonomous navigation, Evangelista has advanced learning-based traversability analysis using pyramidal 3D feature fusion on polar grids, enabling reliable real-time performance even on CPU-constrained platforms — a significant practical achievement for self-driving systems. His human-robot collaboration research, including multi-view human parsing and the DrapeBot cooperative assembly system, highlights his commitment to safe, perceptive robots working alongside humans. With a body of work bridging fundamental calibration theory and applied industrial robotics, Evangelista represents an emerging voice in intelligent automation research.
Research Focus
Key Achievements
Top Papers
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- 33D Mapping of X-Ray Images in Inspections of Aerospace Parts6 citations · 2020
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- 7Multi-view Human Parsing for Human-Robot Collaboration3 citations · 2021
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