Ciro Potena — accepted AI-conference papers

2021

MP-STSP: A Multi-Platform Steiner Traveling Salesman Problem Formulation for Precision Agriculture in Orchards

Renzo Fabrizio Carpio, Jacopo Maiolini, Ciro Potena, Emanuele Garone, Giovanni Ulivi, Andrea Gasparn

ICRA 2021poster

In this work, we propose a global planning strategy specifically designed for precision agriculture settings, where field activities may have different requirements ranging from a full orchard inspection to sparse targeted per-plant interventions. This global planning strategy is formulated as a nov…

Cited by 7 Source ↗Scholar ↗

2020

A Navigation Architecture for Ackermann Vehicles in Precision Farming

Renzo Fabrizio Carpio, Ciro Potena, Jacopo Maiolini, Giovanni Ulivi, Nicolás Bono Rosselló, Emanuele Garone +1

RA-L 2020

In this letter, inspired by the needs of the European H2020 Project PANTHEON <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we propose a full navigation stack purposely designed for the autonomous navigation of Ackermann steering vehicles in

Cited by 42 Source ↗Scholar ↗

2019

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

Ciro Potena, Raghav Khanna, Juan I. Nieto, Roland Siegwart, Daniele Nardi, Alberto Pretto

RA-L 2019

The combination of aerial survey capabilities of unmanned aerial vehicles (UAVs) with targeted intervention abilities of agricultural unmanned ground vehicles (UGVs) can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updatin

Cited by 81 Source ↗Scholar ↗

2018

An Effective Multi-Cue Positioning System for Agricultural Robotics

Marco Imperoli, Ciro Potena, Daniele Nardi, Giorgio Grisetti, Alberto Pretto

RA-L 2018

The self-localization capability is a crucial component for Unmanned Ground Vehicles in farming applications. Approaches based solely on visual cues or on a low-cost Global Positioning System (GPS) are easily prone to fail in such scenarios. In this letter, we present a robust and accurate three-dim

Cited by 41 Source ↗Scholar ↗

2017

Automatic model based dataset generation for fast and accurate crop and weeds detection

Maurilio Di Cicco, Ciro Potena, Giorgio Grisetti, Alberto Pretto

IROS 2017poster

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models…

Cited by 192 Source ↗Scholar ↗