@article{felicioni2024integrating,
author={Felicioni, Simone and Burani, Elena and Leomanni, Mirko and Fravolini, Mario Luca and Valigi, Paolo and Costante, Gabriele},
booktitle={2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
title={Integrating Occupancy Grid with Semantic Road Information for Autonomous Navigation in Urban Scenarios: A Benchmark Study},
year={2024},
volume={},
number={},
pages={2665-2671},
keywords={Point cloud compression;Pedestrians;Navigation;Roads;Semantic segmentation;Semantics;Benchmark testing;Advanced driver assistance systems;Vehicle dynamics;Autonomous robots},
doi={10.1109/CASE59546.2024.10711541}}

@article{denarda2024refined,
author={Denarda, Alessandro Rocco and Crocetti, Francesco and Brilli, Raffaele and Costante, Gabriele and Valigi, Paolo and Fravolini, Mario Luca},
journal={IEEE Transactions on AgriFood Electronics},
title={Refined Weakly Supervised Yield Estimation Through the MiniLabelNet-Count Framework},
year={2024},
volume={},
number={},
pages={1-9},
doi={10.1109/TAFE.2024.3419709}}

@article{dionigi2024dvat,
author={Dionigi, Alberto and Felicioni, Simone and Leomanni, Mirko and Costante, Gabriele}, journal={IEEE Robotics and Automation Letters}, title={D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles}, year={2024}, volume={}, number={}, pages={1-8}, keywords={Target tracking;Cameras;Visualization;Robots;Angular velocity;Training;Aerospace electronics;Visual Tracking;Reinforcement Learning;Visual Servoing;Aerial Systems: Applications}, doi={10.1109/LRA.2024.3385700}}

@article{felicioni2024vision,
author={Felicioni, Simone and Rizzo, Biagio Maria and Tortorici, Claudio and Costante, Gabriele},
journal={IEEE Robotics and Automation Letters},
title={Vision-Based Topological Localization for MAVs},
year={2024},
volume={9},
number={2},
pages={1158-1165},
doi={10.1109/LRA.2023.3341758}}

@article{brilli2023monocular, title={Monocular Reactive Collision Avoidance for MAV Teleoperation with Deep Reinforcement Learning}, author={Raffaele Brilli; Marco Legittimo; Francesco Crocetti; Mirko Leomanni; Mario Luca Fravolini; Gabriele Costante}, journal={2023 IEEE International Conference on Robotics and Automation (ICRA)}, pages={12535-12541}, year={2023} }

@article{mollica2023integrating, title={Integrating Sparse Learning-Based Feature Detectors into Simultaneous Localization and Mapping: A Benchmark Study}, author={Mollica, Giuseppe and Legittimo, Marco and Dionigi, Alberto and Costante, Gabriele and Valigi, Paolo}, journal={Sensors}, volume={23}, number={4}, year={2023} }

@article{mollica2023mavied, title={MA-VIED: A Multisensor Automotive Visual Inertial Event Dataset}, author={Mollica, Giuseppe and Felicioni, Simone and Legittimo, Marco and Meli, Leonardo and Costante, Gabriele and Valigi, Paolo}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2023} }

@article{crocetti2023ard, title={ARD-VO: Agricultural robot data set of vineyards and olive groves}, author={Crocetti, Francesco and Bellocchio, Enrico and Dionigi, Alberto and Felicioni, Simone and Costante, Gabriele and Fravolini, Mario L and Valigi, Paolo}, journal={Journal of Field Robotics}, year={2023}, publisher={Wiley Online Library} }

@article{legittimo23benchmark, AUTHOR = {Legittimo, Marco and Felicioni, Simone and Bagni, Fabio and Tagliavini, Andrea and Dionigi, Alberto and Gatti, Francesco and Verucchi, Micaela and Costante, Gabriele and Bertogna, Marko}, TITLE = {A benchmark analysis of data‐driven and geometric approaches for robot ego‐motion estimation}, YEAR = {2023}, JOURNAL = {Journal of Field Robotics} }

@article{cartocci22robust, AUTHOR = {Cartocci, Nicholas and Crocetti, Francesco and Costante, Gabriele and Valigi, Paolo and Fravolini, Mario L.}, TITLE = {Robust Multiple Fault Isolation Based on Partial-orthogonality Criteria}, JOURNAL = {International Journal of Control, Automation and Systems} }

@article{cartocci22datadriven, AUTHOR = {Cartocci, Nicholas and Napolitano, Marcello R. and Crocetti, Francesco and Costante, Gabriele and Valigi, Paolo and Fravolini, Mario L.}, TITLE = {Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods}, JOURNAL = {Sensors}, VOLUME = {22}, YEAR = {2022}, NUMBER = {7}, ARTICLE-NUMBER = {2635}, URL = {https://www.mdpi.com/1424-8220/22/7/2635}, ISSN = {1424-8220}, ABSTRACT = {Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem. Although very powerful, these techniques require faulty data samples that are representative of any fault scenario. Additionally, ML techniques suffer from issues related to overfitting and unpredictable performance in regions which are not fully explored in the training phase. This paper proposes a non-linear additive model to characterize the non-linear redundancy relationships among the system signals. Using the multivariate adaptive regression splines (MARS) algorithm, these relationships are identified directly from the data. Next, the non-linear redundancy relationships are linearized to derive a local time-dependent fault signature matrix. The faulty sensor can then be isolated by measuring the angular distance between the column vectors of the fault signature matrix and the primary residual vector. A quantitative analysis of fault isolation and fault estimation performance is performed by exploiting real data from multiple flights of a semi-autonomous aircraft, thus allowing a detailed quantitative comparison with state-of-the-art machine-learning-based fault diagnosis algorithms.}, DOI = {10.3390/s22072635} }

@article{dionigi22evat, author={Dionigi, Alberto and Devo, Alessandro and Guiducci, Leonardo and Costante, Gabriele}, journal={IEEE Robotics and Automation Letters}, title={E-VAT: An Asymmetric End-to-End Approach to Visual Active Exploration and Tracking}, year={2022}, volume={7}, number={2}, pages={4259-4266}, doi={10.1109/LRA.2022.3150866}}

@article{devo22autonomous, author={Devo, Alessandro and Mao, Jeffrey and Costante, Gabriele and Loianno, Giuseppe}, journal={IEEE Robotics and Automation Letters}, title={Autonomous Single-Image Drone Exploration With Deep Reinforcement Learning and Mixed Reality}, year={2022}, volume={7}, number={2}, pages={5031-5038}, doi={10.1109/LRA.2022.3154019}}

@article{cartocci2022aircraf, title = {Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria}, journal = {Mechanical Systems and Signal Processing}, volume = {170}, pages = {108668}, year = {2022}, issn = {0888-3270}, doi = {https://doi.org/10.1016/j.ymssp.2021.108668}, url = {https://www.sciencedirect.com/science/article/pii/S0888327021009924}, author = {Nicholas Cartocci and Marcello R. Napolitano and Gabriele Costante and Paolo Valigi and Mario L. Fravolini} }

@article{BELLOCCHIO2022novel,
title = {A novel vision-based weakly supervised framework for autonomous yield estimation in agricultural applications},
journal = {Engineering Applications of Artificial Intelligence},
volume = {109},
pages = {104615},
year = {2022},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2021.104615},
url = {https://www.sciencedirect.com/science/article/pii/S0952197621004310},
author = {Enrico Bellocchio and Francesco Crocetti and Gabriele Costante and Mario Luca Fravolini and Paolo Valigi},
keywords = {Autonomous systems, Automatic yield estimation, Weakly-supervised learning, Visual learning},
abstract = {Autonomous systems have been established as a ground-breaking technology in agriculture, particularly for resource optimization and labor savings. However, even those solutions that are limited to monitoring activities, such as yield estimation, rely on costly robotic platforms equipped with a series of range devices (e.g., LIDAR and GPS-RTK). Recently, vision-based strategies have gained considerable attention as a less expensive and more efficient alternative, capable to be on par with or even surpass approaches that benefit from range sensors. Nonetheless, they exploit deep learning methodologies, which require burdensome labeling procedures to perform training. To address these shortcomings, we present a novel approach that performs yield estimation requiring only a monocular camera and needs a limited amount of supervision information. It detects, locates and maps fruits and tree canopies to estimate the total yield of a specific crop. To keep the image labeling effort to a minimum, we propose a weakly-supervision paradigm that only requires a simple binary label encoding the presence or the absence of fruits in the training images. Our approach does not make any assumptions on the underlying platform, i.e., it can be used by collecting images either with a hand-held camera or with an autonomous robot. Therefore, we are able to considerably reduce the deployment time, the energy and the cost of the overall yield estimation system. At the same time, we keep the performance comparable to both vision-based fully supervised baselines (which require costly labeling operations) and classical systems that rely on more expensive and power-demanding sensors.}
}

@article{cartocci22linear, author = {Cartocci, Nicholas and Monarca, Agnese and Costante, Gabriele and Fravolini, mario and Dogan, Kadriye Merve and Yucelen, Tansel}, year = {2022}, month = {01}, pages = {}, title = {Linear Control of a Nonlinear Aerospace System via Extended Dynamic Mode Decomposition}, doi = {10.2514/6.2022-2046} }

@article{cartocci2021datadriven, author={Cartocci, N. and Crocetti, F. and Costante, G. and Valigi, P. and Napolitano, M.R. and Fravolini, M.L.}, booktitle={2021 20th International Conference on Advanced Robotics (ICAR)}, title={Data-Driven Sensor Fault Diagnosis Based on Nonlinear Additive Models and Local Fault Sensitivity}, year={2021}, volume={}, number={}, pages={750-756}, doi={10.1109/ICAR53236.2021.9659449}}

@article{brilli2021monocular, author={Brilli, Raffaele and Pozzi, Maria and Giorgetti, Folco and Fravolini, Mario Luca and Valigi, Paolo and Prattichizzo, Domenico and Costante, Gabriele}, booktitle={2021 20th International Conference on Advanced Robotics (ICAR)}, title={Monocular Reactive Collision Avoidance Based on Force Fields for Enhancing the Teleoperation of MAVs}, year={2021}, volume={}, number={}, pages={91-98}, doi={10.1109/ICAR53236.2021.9659337}}

@article{felicioni2021goln, author={Felicioni, Simone and Legittimo, Marco and Fravolini, Mario Luca and Costante, Gabriele}, booktitle={2021 20th International Conference on Advanced Robotics (ICAR)}, title={GOLN: Graph Object-based Localization Network}, year={2021}, volume={}, number={}, pages={849-856}, doi={10.1109/ICAR53236.2021.9659450}}

@article{cartocci2021robust, author={Cartocci, N. and Napolitano, M. R. and Costante, G. and Crocetti, F. and Valigi, P. and Fravolini, M. L.}, booktitle={2021 29th Mediterranean Conference on Control and Automation (MED)}, title={A Robust Data-Driven Fault Diagnosis scheme based on Recursive Dempster–Shafer Combination Rule}, year={2021}, volume={}, number={}, pages={1070-1075}, doi={10.1109/MED51440.2021.9480256}}

@article{DEVO2021enhancing,
title = {Enhancing continuous control of mobile robots for end-to-end visual active tracking},
journal = {Robotics and Autonomous Systems},
volume = {142},
pages = {103799},
year = {2021},
issn = {0921-8890},
doi = {https://doi.org/10.1016/j.robot.2021.103799},
url = {https://www.sciencedirect.com/science/article/pii/S0921889021000841},
author = {Alessandro Devo and Alberto Dionigi and Gabriele Costante},
keywords = {Visual active tracking, Deep learning for robotic applications, Reinforcement learning},
abstract = {In the last decades, visual target tracking has been one of the primary research interests of the Robotics research community. The recent advances in Deep Learning technologies have made the exploitation of visual tracking approaches effective and possible in a wide variety of applications, ranging from automotive to surveillance and human assistance. However, the majority of the existing works focus exclusively on passive visual tracking, i.e., tracking elements in sequences of images by assuming that no actions can be taken to adapt the camera position to the motion of the tracked entity. On the contrary, in this work, we address visual active tracking, in which the tracker has to actively search for and track a specified target. Current State-of-the-Art approaches use Deep Reinforcement Learning (DRL) techniques to address the problem in an end-to-end manner. However, two main problems arise: (i) most of the contributions focus only on discrete action spaces, and the ones that consider continuous control do not achieve the same level of performance; and (ii) if not properly tuned, DRL models can be challenging to train, resulting in considerably slow learning progress and poor final performance. To address these challenges, we propose a novel DRL-based visual active tracking system that provides continuous action policies. To accelerate training and improve the overall performance, we introduce additional objective functions and a Heuristic Trajectory Generator (HTG) to facilitate learning. Through extensive experimentation, we show that our method can reach and surpass other State-of-the-Art approaches performances, and demonstrate that, even if trained exclusively in simulation, it can successfully perform visual active tracking even in real scenarios.}
}

@article{crocetti2021tire, author={Crocetti, F. and Costante, G. and Fravolini, M.L. and Valigi, P.}, booktitle={2021 29th Mediterranean Conference on Control and Automation (MED)}, title={Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system}, year={2021}, volume={}, number={}, pages={330-335}, doi={10.1109/MED51440.2021.9480241}}

@article{cartocci2021comprehensive, AUTHOR = {Cartocci, Nicholas and Napolitano, Marcello R. and Costante, Gabriele and Fravolini, Mario L.}, TITLE = {A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation}, JOURNAL = {Sensors}, VOLUME = {21}, YEAR = {2021}, NUMBER = {5}, ARTICLE-NUMBER = {1645}, URL = {https://www.mdpi.com/1424-8220/21/5/1645}, PubMedID = {33652944}, ISSN = {1424-8220} DOI = {10.3390/s21051645} }

@article{Cascianelli2020Databased, title={Data‐based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering}, author={Silvia Cascianelli and Gabriele Costante and Francesco Crocetti and Elisa Ricci and Paolo Valigi and Mario Luca Fravolini}, journal={Asian Journal of Control}, year={2020} }

@article{crocetti2020datadriven, author={Crocetti, F. and Costante, G. and Fravolini, M.L. and Valigi, P.}, booktitle={2020 28th Mediterranean Conference on Control and Automation (MED)}, title={A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions}, year={2020}, volume={}, number={}, pages={496-501}, doi={10.1109/MED48518.2020.9182792}}

@inproceedings{fravolini2021safe,
author = {Fravolini, mario and Cartocci, Nicholas and Dogan, Kadriye Merve and Yucelen, Tansel},
year = {2021},
month = {01},
pages = {},
title = {A Safe Learning Model Reference Adaptive Controller for Uncertain Aircrafts Models},
doi = {10.2514/6.2021-0532}
}

@inproceedings{taraglio2020thesmart, author = {Taraglio, S and Chiesa, S and Nanni, V and Pieroni, F and Pollino, M and Di Pietro, A and Montorselli, S and Bellocchio, E and Costante, G and Fravolini, ML and Valigi, P}, year = {2020}, month = {}, pages = {}, title = {The Smart Road Project at ENEA} }

@inproceedings{bellocchio2020agrobot, author = {Bellocchio, E and Bruni, A and Cartocci, Nicholas and Costante, Gabriele and Crocetti, F and Longhi, A and Pacicco, L and Palliotti, A and Petacchi, R and Radicioni, F and Rinaldi, M and Santucci, G and Sdoga, A and Tosi, G and Valigi, Paolo and Bisio, M}, year = {2020}, month = {12}, pages = {}, title = {Agrobot: autonomous robots to support economic growth and environmental sustainability of Umbria’s agriculture} }

@article{cartocci2020pca, author={Cartocci, N. and Costante, G. and Napolitano, M.R. and Valigi, P. and Crocetti, F. and Fravolini, M.L.}, booktitle={2020 28th Mediterranean Conference on Control and Automation (MED)}, title={PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis}, year={2020}, volume={}, number={}, pages={550-555}, doi={10.1109/MED48518.2020.9182973}}

@ARTICLE{costante2020uncertainty, author={Costante, Gabriele and Mancini, Michele}, journal={IEEE Transactions on Robotics}, title={Uncertainty Estimation for Data-Driven Visual Odometry}, year={2020}, volume={36}, number={6}, pages={1738-1757}, doi={10.1109/TRO.2020.3001674}}

@article{devo2020towards, author={Devo, Alessandro and Mezzetti, Giacomo and Costante, Gabriele and Fravolini, Mario L. and Valigi, Paolo}, journal={IEEE Transactions on Robotics}, title={Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning}, year={2020}, volume={36}, number={5}, pages={1546-1561}, doi={10.1109/TRO.2020.2994002}}

@article{bellocchio2020combining, title={Combining Domain Adaptation and Spatial Consistency for Unseen Fruits Counting: A Quasi-Unsupervised Approach}, author={Bellocchio, Enrico and Costante, Gabriele and Cascianelli, Silvia and Fravolini, Mario Luca and Valigi, Paolo}, journal={IEEE Robotics and Automation Letters}, volume={5}, number={2}, pages={1079–1086}, year={2020}, publisher={IEEE} }

@article{devo2020deep, title={Deep Reinforcement Learning for Instruction Following Visual Navigation in 3D Maze-Like Environments}, author={Devo, Alessandro and Costante, Gabriele and Valigi, Paolo}, journal={IEEE Robotics and Automation Letters}, volume={5}, number={2}, pages={1175–1182}, year={2020}, publisher={IEEE} }

@article{fravolini2020quantification, author = {Fravolini, Mario and Cartocci, Nicholas and Dogan, Kadriye Merve and Yucelen, Tansel}, year = {2020}, month = {01}, pages = {}, title = {Quantification of Tolerable Parametric and Dynamic Uncertainty for Robust MRAC Systems}, doi = {10.2514/6.2020-1338} }

@article{cascianelli2019databased, author={Cascianelli, Silvia and Crocetti, Francesco and Costante, Gabriele and Valigi, Paolo and Fravolini, Mario Luca}, booktitle={2019 International Conference on Control, Automation and Diagnosis (ICCAD)}, title={Data-Based Design of Robust Fault Isolation Residuals Using LASSO optimization}, year={2019}, volume={}, number={}, pages={1-6}, doi={10.1109/ICCAD46983.2019.9037959}}

@article{cascianelli2019role, title={The Role of the Input in Natural Language Video Description}, author={Cascianelli, Silvia and Costante, Gabriele and Devo, Alessandro and Ciarfuglia, Thomas A and Valigi, Paolo and Fravolini, Mario L}, journal={IEEE Transactions on Multimedia}, volume={22}, number={1}, pages={271–283}, year={2019}, publisher={IEEE} }

@article{bellocchio2019weakly, title={Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency}, author={Bellocchio, Enrico and Ciarfuglia, Thomas A and Costante, Gabriele and Valigi, Paolo}, journal={IEEE Robotics and Automation Letters}, volume={4}, number={3}, pages={2348–2355}, year={2019}, publisher={IEEE} }