Deep Learning, more specifically Convolutional Neural Networks (CNNs), applied in precision farming scenarios for crop/weed detection and classification. I am also one of the principal investigators in the Flourish Project.
My main passion: Unmanned Aerial Vehicles (UAVs). I am currently investigating new methods and algorithms for the Control and the Navigation of such vehicles. I choose as main testbed scenario the Vision-Based Navigation. A possible interesting application is the Autonomous Crop Row following in a farming field.
I am also involved in the FlexSight project, allowing a robot to perceive rigid and deformable objects.
For a complete list of my publications look at my Google Scholar profile.
- "Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement" C. Potena, B. Della Corte, D. Nardi, G, Grisetti and A. Pretto. IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), Brisbane, 2018 Best Student Paper Award Finalist. (code, pdf, bibtex)
- "An Effective Multi-Cue Positioning System for Agricultural Robotics", M. Imperoli, C. Potena, D. Nardi, G. Grisetti and A. Pretto. arXiv preprint arXiv:1803.00954, 2018. (pdf, video, bibtex)
- "Effective Target Aware Visual Navigation for UAVs" C. Potena, D. Nardi and A. Pretto. The European Conference on Mobile Robotics (ECMR), Paris, 2017. (pdf, bibtex)
- "Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection" M. Di Cicco, C. Potena, G. Grisetti, A.Pretto. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, 2017. (pdf, video, bibtex)
Department of Computer, Control, and Management Engineering (DIAG) "Antonio Ruberti" at Sapienza University of Rome.
Via Ariosto, 25. 00185 Rome, Italy.
Contact me at: potena[at]diag[dot]uniroma1[dot]it