Alberto Beinat
Papers
3
Total Citations
41
H-Index
3
About
Alberto Beinat is a researcher at the forefront of geomatics and robotic mapping, specializing in the development and assessment of mobile systems for high-precision 3D data acquisition. His work bridges the gap between traditional surveying and autonomous robotics, with a core focus on evaluating the performance, repeatability, and feasibility of SLAM-based mapping technologies. Beinat’s most cited paper (30 citations) provides a rigorous quantitative investigation of a mobile robotic system for 3D mapping, demonstrating its potential to automate data collection more efficiently than manual topographic methods. He has also conducted comparative studies between handheld and mobile robotic mapping systems, and explored the accuracy of as-built modelling from SLAM-derived point clouds—a critical contribution for architectural and construction surveying. By systematically assessing these emerging technologies, Beinat is helping to define best practices for their deployment in real-world environments. His work is particularly valuable for researchers and practitioners seeking reliable, automated solutions for geometric data acquisition, and his preliminary findings on SLAM-based modelling are paving the way for more efficient, accurate, and scalable mapping workflows in the built environment.
Research Focus
Key Achievements
Top Papers
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