Simone Mentasti

Papers

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About

Simone Mentasti is a robotics researcher whose work spans autonomous navigation, perception, and agricultural robotics. His key contributions lie in developing intelligent systems for mobile robots operating in dynamic, real-world environments—from crowded pedestrian spaces to agricultural fields. Mentasti’s research on pedestrian trajectory prediction introduced a spatio-temporal graph network capable of handling incomplete trajectory inputs, a critical advancement for robot navigation in environments where pedestrians may be temporarily occluded. In the domain of loop closure detection, he developed RadarLCD, a learnable radar-based pipeline that enhances a robot’s ability to recognize previously visited locations—a fundamental capability for long-term autonomous navigation. His work in agricultural robotics addresses the practical challenge of detecting and mapping crop weeds and litter, enabling precision farming robots to perform time-consuming tasks like field monitoring with high accuracy. With each of his most-cited papers garnering 3 citations, Mentasti’s research is gaining recognition for its practical impact on real-world robotic systems. His contributions are particularly valuable for students and researchers interested in perception for autonomous navigation, sensor fusion, and field robotics.