Rafael Roberto

Papers

Total Citations

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About

Rafael Roberto is a researcher whose work sits at the intersection of computer vision, augmented reality (AR), and spatial intelligence. His primary contributions lie in advancing real-time tracking and localization systems, with a particular focus on making these technologies robust and generalizable. His most cited work, "3D pedestrian localization using multiple cameras: a generalizable approach" (2022, 11 citations), addresses a critical challenge in autonomous systems and smart environments: accurately tracking people in 3D space without relying on scene-specific training data. This generalizability is key for deploying systems in unfamiliar or dynamic settings. Roberto’s earlier foundational work, "Life Cycle of a SLAM System: Implementation, Evaluation and Port to the Project Tango Device" (2016, 5 citations), provides a comprehensive guide to building and adapting Simultaneous Localization and Mapping (SLAM) systems—a core technology for AR and robotics. By detailing the porting process to Google’s Project Tango, he helped bridge the gap between theoretical SLAM algorithms and practical, device-level implementation. Through these contributions, Roberto has demonstrated a clear commitment to creating scalable, real-world solutions for spatial understanding and human tracking.