Miguel Ribo

VRVis GmbH (Austria), Graz University of Technology, Instituto Superior Técnico

Papers

Total Citations

167

H-Index

About

Miguel Ribo’s research lies at the intersection of robotics perception, sensor fusion, and real-time vision systems, with a focus on enabling machines to navigate and track with high precision under uncertainty. His most influential work, “A comparison of three uncertainty calculi for building sonar-based occupancy grids” (89 citations), established foundational methods for probabilistic mapping in mobile robotics, directly addressing how robots can interpret noisy sensor data to understand their environment. Ribo also pioneered high-speed vision for tracking, co-developing a novel CMOS camera system (58 citations) that combined off-the-shelf components—CMOS sensors, FPGA logic, and USB 2.0—to achieve real-time pixel-level access, a breakthrough for applications in robotics and augmented reality. His work on hybrid tracking, detailed in “A Flexible Software Architecture for Hybrid Tracking” (15 citations), fuses vision and inertial data for robust, self-contained pose estimation, advancing navigation and AR systems. Earlier in his career, Ribo tackled motion planning under sensor uncertainty, designing algorithms for precise mobile robot localization using extended Kalman filtering. Through these contributions, Ribo has shaped how robots perceive, track, and move, leaving a lasting impact on autonomous systems and sensor-driven technologies.