Daniela Rato

Papers

Total Citations

H-Index

About

Daniela Rato is a researcher specializing in multi-modal, multi-sensor calibration for robotics and industrial automation. Her work focuses on developing frameworks that enable precise extrinsic calibration—the process of determining the spatial relationships between different sensors—which is critical for creating accurate, fused representations of environments. Her major contributions include the ATOM framework, a general calibration system for multi-modal, multi-sensor setups that has garnered 25 citations since 2022. She has also advanced calibration methods for collaborative robotic industrial cells, where human-robot safety depends on comprehensive spatial perception, as detailed in her 2022 paper on sensor-to-pattern calibration (14 citations). Her 2020 work on using extended sets of pairwise geometric transformations for multi-sensor calibration (8 citations) further demonstrates her systematic approach to solving complex alignment problems. Rato’s research is particularly impactful in enabling safer, more efficient human-robot collaboration in industrial settings, where precise sensor fusion is essential for real-time monitoring and interaction. Her frameworks provide foundational tools for researchers and engineers working on autonomous systems, mobile robotics, and highly monitored environments.