@article {pierdicca2016virtual,
	title = {Virtual reconstruction of archaeological heritage using a combination of photogrammetric techniques: Huaca Arco Iris, Chan Chan, Peru},
	journal = {Digital Applications in Archaeology and Cultural Heritage},
	volume = {3},
	number = {3},
	year = {2016},
	pages = {80{\textendash}90},
	publisher = {Elsevier},
	author = {Roberto Pierdicca and Emanuele Frontoni and Eva Savina Malinverni and F. Colosi and R. Orazi}
}
@article {cocchioni2016visual,
	title = {Visual Based Landing for an Unmanned Quadrotor},
	journal = {Journal of Intelligent \& Robotic Systems},
	volume = {84},
	number = {1-4},
	year = {2016},
	pages = {511{\textendash}528},
	publisher = {Springer Netherlands},
	author = {Francesco Cocchioni and Emanuele Frontoni and Ippoliti, Gianluca and Sauro Longhi and Adriano Mancini and Primo Zingaretti}
}
@article {Cocchioni2015,
	title = {Visual Based Landing for an Unmanned Quadrotor},
	journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
	year = {2015},
	note = {cited By 0; Article in Press},
	doi = {10.1007/s10846-015-0271-6},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84941686223\&partnerID=40\&md5=9ccfc812909e8a74b64d123902639160},
	author = {Francesco Cocchioni and Emanuele Frontoni and Ippoliti, G. and Sauro Longhi and Adriano Mancini and Primo Zingaretti}
}
@article {Cesetti2011157,
	title = {A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications},
	journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
	volume = {61},
	number = {1-4},
	year = {2011},
	note = {cited By 10},
	pages = {157-168},
	abstract = {The combination of photogrammetric aerial and terrestrial recording methods can provide new opportunities for photogrammetric applications. A UAV (Unmanned Aerial Vehicle), in our case a helicopter system, can cover both the aerial and quasi-terrestrial image acquisition methods. A UAV can be equipped with an on-board high resolution camera and a priori knowledge of the operating area where to perform photogrammetric tasks. In this general scenario our paper proposes vision-based techniques for localizing a UAV. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. The novel idea is to perform global localization, position tracking and localization failure recovery (kidnapping) based only on visual matching between current view and available georeferenced satellite images. The matching is based on SIFT features and the system estimates the position of the UAV and its altitude on the base of the reference image. The vision system replaces the GPS signal combining position information from visual odometry and georeferenced imagery. Georeferenced satellite or aerial images must be available on-board beforehand or downloaded during the flight. The growing availability of high resolution satellite images (e.g., provided by Google Earth or other local information sources) makes this topic very interesting and timely. Experiments with both synthetic (i.e., taken from satellites or datasets and pre elaborated) and real world images have been performed to test the accuracy and the robustness of our method. Results show sufficient performance if compared with common GPS systems and give a good performance also in the altitude estimation, even if in this last case there are only preliminary results. {\textcopyright} 2010 Springer Science+Business Media B.V.},
	doi = {10.1007/s10846-010-9489-5},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79951511981\&partnerID=40\&md5=22fb7414e9a79d5c70082f7e0e8c0684},
	author = {Cesetti, A. and Emanuele Frontoni and Adriano Mancini and Ascani, A. and Primo Zingaretti and Sauro Longhi}
}
@article {Cesetti2010233,
	title = {A Vision-based guidance system for UAV navigation and safe landing using natural landmarks},
	journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
	volume = {57},
	number = {1-4},
	year = {2010},
	note = {cited By 45},
	pages = {233-257},
	abstract = {In this paper a vision-based approach for guidance and safe landing of an Unmanned Aerial Vehicle (UAV) is proposed. The UAV is required to navigate from an initial to a final position in a partially known environment. The guidance system allows a remote user to define target areas from a high resolution aerial or satellite image to determine either the waypoints of the navigation trajectory or the landing area. A feature-based image-matching algorithm finds the natural landmarks and gives feedbacks to an onboard, hierarchical, behaviour-based control system for autonomous navigation and landing. Two algorithms for safe landing area detection are also proposed, based on a feature optical flow analysis. The main novelty is in the vision-based architecture, extensively tested on a helicopter, which, in particular, does not require any artificial landmark (e.g., helipad). Results show the appropriateness of the vision-based approach, which is robust to occlusions and light variations. {\textcopyright} 2009 Springer Science+Business Media B.V.},
	doi = {10.1007/s10846-009-9373-3},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84893415343\&partnerID=40\&md5=1af91a05c14c688e97131ab24f43e2bd},
	author = {Cesetti, A. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti and Sauro Longhi}
}
@conference {Cesetti2009910,
	title = {Vision-based autonomous navigation and landing of an unmanned aerial vehicle using natural landmarks},
	booktitle = {2009 17th Mediterranean Conference on Control and Automation, MED 2009},
	year = {2009},
	note = {cited By 13},
	pages = {910-915},
	abstract = {This paper presents the design and implementation of a vision-based navigation and landing algorithm for an autonomous helicopter. The vision system allows to define target areas from a high resolution aerial or satellite image to determine the waypoints of the navigation trajectory or the landing area. The helicopter is required to navigate from an initial position to a final position in a partially known environment using GPS and vision, to locate a landing target (a helipad of a known shape or a natural landmark) and to land on it. The vision system, using a feature-based image matching algorithm, finds the area and gives feedbacks to the control system for autonomous landing. Vision is used for accurate target detection, recognition and tracking. The helicopter updates its landing target parameters owing to vision and uses an on board behavior-based controller to follow a path to the landing site. Results show the appropriateness of the vision-based approach that does not require any artificial landmark (e.g., helipad) and is quite robust to occlusions, light variations and seasonal changes (e.g., brown or green leaves). {\textcopyright} 2009 IEEE.},
	doi = {10.1109/MED.2009.5164661},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84885338997\&partnerID=40\&md5=e544d46c421bd62be9c0ad9fa4ac016b},
	author = {Cesetti, A. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti and Sauro Longhi}
}
@conference {Zingaretti2007,
	title = {Vision and sonar sensor fusion for mobile robot localization in aliased environments},
	booktitle = {Proceedings of the 2nd IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2006},
	year = {2007},
	note = {cited By 9},
	abstract = {Monte Carlo Localization (MCL) is a common method for self-localization of a mobile robot under the assumption that a map of the environment is available. Original implementations used range sensors like laser scanners and sonar sensors. Recently, localization approaches using vision sensors have been developed with good results. In this paper we compare vision-based with sonar-based MCL approaches in terms of localization accuracy. In particular, we show how in an environment with high perceptual aliasing like our department both approaches bear certain weaknesses while by combining vision and sonar sensors the respective localization errors decrease and overall accuracy is improved.},
	doi = {10.1109/MESA.2006.296971},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-44949256550\&partnerID=40\&md5=22fa07327960746e83a4f0b3c0ce5f46},
	author = {Primo Zingaretti and Emanuele Frontoni}
}
@conference {Frontoni2007,
	title = {Vision based approach for active selection of robot{\textquoteright}s localization action},
	booktitle = {2007 Mediterranean Conference on Control and Automation, MED},
	year = {2007},
	note = {cited By 4},
	abstract = {The paper presents a mobile robot localization system that integrates Monte-Carlo Localization (MCL) with an active action-selection approach based on an aliasing map. The main novelties of the approach are: the off-line evaluation of the perceptual aliasing of the environment; the use of this knowledge to actively perform the localization processes; the use of an improved SIFT feature extractor to aliasing map evaluation and to measure image similarity. Results, obtained in a real scenario using a real robot, show improved performances in the number of steps needed to correctly localize the robot and in the localization error, compared with the classic MCL approach. Also better performances in computational time due to improvements in the vision system are shown. {\textcopyright}2007 IEEE.},
	doi = {10.1109/MED.2007.4433677},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-50249111061\&partnerID=40\&md5=c6e81b44708accfe6472fb11aa06e510},
	author = {Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Frontoni2007197,
	title = {Visual feature group matching for autonomous robot localization},
	booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007},
	year = {2007},
	note = {cited By 2},
	pages = {197-202},
	abstract = {The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot vision, object recognition, motion estimation, etc. In this work, we propose a SIFT improvement that makes feature extraction and matching more robust, adding a feature group matching layer, which takes into account mutual spatial relations between features. The feature group matching is very fast to be computed and leads to interesting results, above all for the absence of outliers. Results of vision based robot localization using the proposed approach are presented. {\textcopyright} 2007 IEEE.},
	doi = {10.1109/ICIAP.2007.4362779},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-48149104519\&partnerID=40\&md5=65dcfdb3e59bdf9ea8eaa2c5d0165770},
	author = {Emanuele Frontoni and Primo Zingaretti}
}
@conference {Frontoni2005347,
	title = {A vision based algorithm for active robot localization},
	booktitle = {Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA},
	year = {2005},
	note = {cited By 10},
	pages = {347-352},
	abstract = {This paper describes a method to localize a mobile robot in a structured indoor environment from visual information provided by omnidirectional images. In particular, an approximate algorithm for active robot localization in partially observable environments based on a POMDP model is introduced. Results from many different tests are presented to prove the setting of algorithm parameters. They show that a localization accuracy of less than 25 cm can be achieved even with a partial knowledge of the environment Besides, the proposed approximated algorithm reduces the computational cost without degrading results. {\textcopyright} 2005 IEEE.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-28444487920\&partnerID=40\&md5=0f5de1c29fccabb1de7c4b94d2a750d0},
	author = {Emanuele Frontoni and Primo Zingaretti}
}
@conference {Baldassarri2003368,
	title = {Visual self-localisation using automatic topology construction},
	booktitle = {Proceedings - 12th International Conference on Image Analysis and Processing, ICIAP 2003},
	year = {2003},
	note = {cited By 0},
	pages = {368-374},
	abstract = {The paper proposes a machine learning method for self-localising a mobile agent, using the images supplied by a single omni-directional camera. The images acquired by the camera may be viewed as an implicit topological representation of the environment. The environment is a priori unknown and the topological representation is derived by unsupervised neural network architecture. The architecture includes a self-organising neural network, and is constituted by a growing neural gas, which is well known for its topology preserving quality. The growth depends on the topology that is not a priori defined, and on the need of discovering it, by the neural network, during the learning. The implemented system is able to recognise correctly the input frames and to reconstruct a topological map of the environment. Each node of the neural network identifies a single zone of the environment and the connections between the nodes correspond to the real space connections in the environment. {\textcopyright} 2003 IEEE.},
	doi = {10.1109/ICIAP.2003.1234077},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890545853\&partnerID=40\&md5=6954a4c9b47cdb3e275eac96afdf95a2},
	author = {Paola Baldassarri and Paolo Puliti and A. Montesanto and Guido Tascini}
}
