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About

Stefano Mafrica is a leading researcher in bio-inspired robotics and visual navigation, specializing in minimalistic optic flow sensors for autonomous vehicles. His work focuses on developing lightweight, adaptive visual systems that enable robots to estimate motion and position in real-time, even under challenging lighting conditions. Mafrica’s major contributions include the design of auto-adaptive pixels based on the Michaelis-Menten law, which allow sensors to operate across a five-decade light level range—from dim indoor spaces to bright outdoor environments. He pioneered a novel cross-correlation optical flow algorithm and integrated it with an extended Kalman filter for car-like robots, enabling accurate velocity and steering angle estimation using only two downward-facing sensors. His papers, such as "Minimalistic optic flow sensors applied to indoor and outdoor visual guidance and odometry on a car-like robot" (20 citations) and "Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot" (17 citations), demonstrate the impact of his work on autonomous navigation. Mafrica’s innovations have practical applications in micro air vehicles and outdoor robots, where GPS is unavailable, making him a key figure in advancing robust, low-cost visual odometry for real-world robotics.