@article {Frontoni2014,
	title = {Feature group matching: A novel method to filter out incorrect local feature matchings},
	journal = {International Journal of Pattern Recognition and Artificial Intelligence},
	volume = {28},
	number = {5},
	year = {2014},
	note = {cited By 4},
	abstract = {The importance of finding correct correspondences between two images is the major aspect in problems such as appearance-based robot localization and content-based image retrieval. Local feature matching has become a commonly used method to compare images, despite being highly probable that at least some of the matchings/correspondences it detects are incorrect. In this paper, we describe a novel approach to local feature matching, named Feature Group Matching (FGM), to select stable features and obtain a more reliable similarity value between two images. The proposed technique is demonstrated to be translational, rotational and scaling invariant. Experimental evaluation was performed on large and heterogeneous datasets of images using SIFT and SURF, the actual state-of-the-art feature extractors. Results show that FGM avoids almost 95\% of incorrect matchings, reduces the visual aliasing (number of images considered similar) and increases both robotic localization and image retrieval accuracy on the average of 13\%. {\textcopyright} 2014 World Scientific Publishing Company.},
	doi = {10.1142/S0218001414500128},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84905462697\&partnerID=40\&md5=e778e6ea38958157d1df890fc014a6e6},
	author = {Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Catani2010319,
	title = {A framework based on vision sensors for the automatic management of exchange parking areas},
	booktitle = {Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2010},
	year = {2010},
	note = {cited By 3},
	pages = {319-324},
	abstract = {This paper proposes a framework for the automatic management of exchange parking areas, usually located in the periphery of large cities. These parks are used for medium/long period stops of private or public vehicles and the subsequent sorting of passengers to public transportation networks. The objective of this paper is to analyze and stress the potential of a framework that exploits only vision sensors, which are very versatile and minimally invasive. Using a visual sensor network and the proposed tracking approach we are able to know and track the position of every bus in the exchange station and to send data to the planning station, which allocates slots for other busses and manages public information. Preliminary results are promising and show the feasibility of the proposed method, so that future research work is directed towards a distributed implementation of the framework with stand-alone and embedded devices under the control of a supervisor. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/MESA.2010.5552047},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77957341506\&partnerID=40\&md5=accfd4f3b19a01e8662f81ce6185545b},
	author = {Ludovico Catani and Emanuele Frontoni and Primo Zingaretti}
}
@article {Mancini2009307,
	title = {A framework for simulation and testing of UAVs in cooperative scenarios},
	journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
	volume = {54},
	number = {1-3 SPEC. ISS.},
	year = {2009},
	note = {cited By 18},
	pages = {307-329},
	abstract = {Today, Unmanned Aerial Vehicles (UAVs) have deeply modified the concepts of surveillance, Search\&Rescue, aerial photogrammetry, mapping, etc. The kinds of missions grow continuously; missions are in most cases performed by a fleet of cooperating autonomous and heterogeneous vehicles. These systems are really complex and it becomes fundamental to simulate any mission stage to exploit benefits of simulations like repeatability, modularity and low cost. In this paper a framework for simulation and testing of UAVs in cooperative scenarios is presented. The framework, based on modularity and stratification in different specialized layers, allows an easy switching from simulated to real environments, thus reducing testing and debugging times, especially in a training context. Results obtained using the proposed framework on some test cases are also reported. {\textcopyright} 2008 Springer Science+Business Media B.V.},
	doi = {10.1007/s10846-008-9268-8},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-56649092912\&partnerID=40\&md5=dc161c1b0a0533432e58fcdf588aec40},
	author = {Adriano Mancini and Cesetti, A. and Iual{\`e}, A. and Emanuele Frontoni and Primo Zingaretti and Sauro Longhi}
}
@conference {Ascani20083933,
	title = {Feature group matching for appearance-based localization},
	booktitle = {2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS},
	year = {2008},
	note = {cited By 24},
	pages = {3933-3938},
	abstract = {Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization tasks. In this paper, we address the issues of appearance-based topological and metric localization by introducing a novel group matching approach to select less but more robust features to match the current robot view with reference images. Feature group matching is based on the consideration that feature descriptors together with spatial relations are more robust than classical approaches. Our datasets, each consisting of a large number of omnidirectional images, have been acquired over different day times (different lighting conditions) both in indoor and outdoor environments. The feature group matching outperforms the SIFT in indoor localization showing better performances both in the case of topological and metric localization. In outdoor SURF remains the best feature extraction method, as reported in literature. {\textcopyright}2008 IEEE.},
	doi = {10.1109/IROS.2008.4651023},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-69549135915\&partnerID=40\&md5=26b54613d0a4e016f6a3b8bec2face46},
	author = {Ascani, A. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@article {Cesetti200817,
	title = {From simulated to real scenarios: A framework for multi-UAVs},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5325 LNAI},
	year = {2008},
	note = {cited By 2},
	pages = {17-28},
	abstract = {In this paper a framework for simulation of Unmanned Aerial Vehicles (UAVs), oriented to rotary wings aerial vehicles, is presented. It allows UAV simulations for stand-alone agents or multi-agents exchanging data in cooperative scenarios. The framework, based on modularity and stratification in different specialized layers, allows an easy switching from simulated to real environments, thus reducing testing and debugging times. CAD modelling supports the framework mainly with respect to extraction of geometrical parameters and virtualization. Useful applications of the framework include pilot training, testing and validation of UAVs control strategies, especially in an educational context, and simulation of complex missions. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	doi = {10.1007/978-3-540-89076-8-6},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-58049115678\&partnerID=40\&md5=8301ffc2a6a97aefca4df012079ad726},
	author = {Cesetti, A. and Adriano Mancini and Emanuele Frontoni and Primo Zingaretti and Sauro Longhi}
}
@conference {Frontoni2006,
	title = {Fast mobile robot localization using low cost sensors},
	booktitle = {IFAC Proceedings Volumes (IFAC-PapersOnline)},
	volume = {8},
	number = {PART 1},
	year = {2006},
	note = {cited By 2},
	abstract = {Bayesian filtering is a well known probabilistic filtering method. Its applications to mobile robot localization are very popular, but an active approach to the problem of localization was never presented. An interesting question is: what is the best action that the robot should choose to localize itself in the minimum number of steps? This paper presents the Fast Particle Filtering (FPF) algorithm to select the best action that allows a fast global localization using particle filtering. The appropriateness of our approach is demonstrated empirically using a mobile robot equipped with low cost sonar sensors in a structured office environment. Comparisons with classical Bayesian filtering approaches are also presented to demonstrate the better performances and the lower computational cost of the FPF algorithm. Copyright {\textcopyright} 2006 IFAC.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80051600774\&partnerID=40\&md5=b6c6e42ff4df40128cbe9bf76d128f44},
	author = {Emanuele Frontoni and Adriano Mancini and Caponetti, F. and Primo Zingaretti}
}
@conference {Frontoni2006,
	title = {A framework for simulations and tests of mobile robotics tasks},
	booktitle = {14th Mediterranean Conference on Control and Automation, MED{\textquoteright}06},
	year = {2006},
	note = {cited By 13},
	abstract = {This paper presents an education framework, developed in Matlab, for studying and experimenting typical mobile robotics tasks such as obstacle avoidance, localization, navigation and SLAM. The most important characteristic of this framework is the ability to easily switch from a simulator to a real robot to tune and test algorithms and to evaluate results in simulated and real environments. The framework is being used with interesting results in robotic courses at the Universit{\`a} Politecnica delle Marche in Ancona, Italy. In the second part of the paper a test case to evaluate an optimization of a Monte Carlo Localization process with sonar sensors is presented.},
	doi = {10.1109/MED.2006.328842},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35949002522\&partnerID=40\&md5=c56db964d779bed90b4aff91801d06cf},
	author = {Emanuele Frontoni and Adriano Mancini and Caponetti, F. and Primo Zingaretti}
}
@article {Zingaretti1998407,
	title = {Fast chain coding of region boundaries},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {20},
	number = {4},
	year = {1998},
	note = {cited By 32},
	pages = {407-415},
	abstract = {A fast single-pass algorithm to convert a multivalued image from a raster-based representation into chain codes is presented. All chain codes are obtained in linear time with respect to the number of chain segments that are generated at each raster according to a set of templates. A formal statement and the complexity and performance analysis of the algorithm are given. {\textcopyright}1998 IEEE.},
	doi = {10.1109/34.677272},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0032047517\&partnerID=40\&md5=efe01ab3fa89f0ff9e24a34413091a2b},
	author = {Primo Zingaretti}
}
