Mateus V. Gasparino
University of Illinois Urbana-Champaign, Universidade de São Paulo
Papers
14
Total Citations
248
H-Index
9
About
Mateus V. Gasparino is a robotics researcher whose work sits at the intersection of autonomous navigation, agricultural robotics, and machine learning. He has made significant contributions to enabling mobile robots to operate in challenging, unstructured environments — particularly beneath crop canopies where conventional positioning systems like GNSS prove unreliable. Gasparino is perhaps best known for the WayFAST framework (2022, 62 citations), a self-supervised traversability prediction system that equips wheeled robots with the ability to autonomously identify safe paths using RGB-D data and prior navigation experience. This work was extended in WayFASTER (2024, 21 citations), which incorporates multi-sensor temporal fusion for heightened environmental awareness. His parallel contributions to agricultural robotics are equally impactful: his visually guided under-canopy navigation system (2021, 53 citations) and LiDAR-based row-following work (2022, 26 citations) have helped establish foundational methods for compact farm robots capable of tasks beyond the reach of drones or large machinery. His CropNav framework and CROW algorithm further demonstrate a commitment to real-world deployability. More recently, his exploration of federated learning for distributed robot navigation signals a broadening research vision. With over 230 cumulative citations, Gasparino is an emerging force in field robotics and precision agriculture.
Research Focus
Key Achievements
Top Papers
- 1WayFAST: Navigation With Predictive Traversability in the Field62 citations · 2022
- 2Learned Visual Navigation for Under-Canopy Agricultural Robots53 citations · 2021
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- 7CropNav: a Framework for Autonomous Navigation in Real Farms12 citations · 2023
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