@conference {Frontoni2010428,
	title = {Robot localization in urban environments using omnidirectional vision sensors and partial heterogeneous apriori knowledge},
	booktitle = {Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2010},
	year = {2010},
	note = {cited By 3},
	pages = {428-433},
	abstract = {This paper addresses the problem of long term mobile robot localization in large urban environments using a partial apriori knowledge made by different kind of images. Typically, GPS is the preferred sensor for outdoor operation. However, using GPS-only localization methods leads to significant performance degradation in urban areas where tall nearby structures obstruct the view of the satellites. In our work, we use omnidirectional vision-based sensors to complement GPS and odometry and provide accurate localization. We also present some novel Monte Carlo Localization optimizations and we introduce the concept of online knowledge acquisition and integration presenting a framework able to perform long term robot localization in real environments. The vision system identifies prominent features in the scene and matches them with a database of geo-referenced features already known (with a partial coverage of the environment and using both directional and omnidirectional images and with different resolutions) or learned and integrated during the localization process (omnidirectional images only). Results of successful robot localization in the old town of Fermo are presented. The whole architecture behaves well also in long term experiments, showing a suitable and good system for real life robot applications with a particular focus on the integration of different knowledge sources. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/MESA.2010.5551994},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77957367131\&partnerID=40\&md5=619cedb309b8e6e9bf546721e565a793},
	author = {Emanuele Frontoni and Ascani, A. and Adriano Mancini and Primo Zingaretti}
}
@conference {Mancini20092544,
	title = {RoboBuntu: A linux distribution for mobile robotics},
	booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
	year = {2009},
	note = {cited By 2},
	pages = {2544-2549},
	abstract = {During last years Linux started to climb the market of operating systems (OSs), and Ubuntu, derived by Debian OS, has become a good alternative to common OSs like Windows XP or Vista. The mobile robotics scientific community makes use of Linux based OSs to avoid the lack of stability that affects Microsoft OSs, especially when real time conditions must be satisfied. In this paper we present the Linux distribution RoboBuntu, acronym formed by the union of ROBOt and uBUNTU, to overcome the almost totally independent robotic software platforms existing today. The key idea behind RoboBuntu is the integration of different tools for mobile robotics into an embedded Ubuntu distribution. Another important characteristics of RoboBuntu is that every "hard step", like installation and configuration of OS and tools, is hidden to common users. In particular, RoboBuntu can be used either by students or researchers, as LiveCd, permanent installation on standard hard drive or, more interesting, on aUSB storage flash disk. {\textcopyright} 2009 IEEE.},
	doi = {10.1109/ROBOT.2009.5152548},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350399144\&partnerID=40\&md5=7ad3e306e93a56d841f7c4fdaf250a03},
	author = {Adriano Mancini and Emanuele Frontoni and Ascani, A. and Primo Zingaretti}
}
@conference {Ascani2008576,
	title = {Robot localization using omnidirectional vision in large and dynamic outdoor environments},
	booktitle = {2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008},
	year = {2008},
	note = {cited By 3},
	pages = {576-581},
	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 present a mobile robot localization system based on local feature matching of omnidirectional images. In particular, we address the issues of appearance-based topological localization by comparing common feature-extractor methods (SIFT and SURF) to select robust features to match the current robot view with reference images. Our datasets, each consisting of a large number of omnidirectional images, have been acquired over different day times (different lighting conditions) and dynamic content in large outdoor environments (over 80.000 m2). Two different approaches (WTA and MCL) were used to evaluate performances, which, in general, are satisfactory. In particular, the use of Monte Carlo particle filtering improves topological localization results for all datasets with all algorithms. {\textcopyright} 2008 IEEE.},
	doi = {10.1109/MESA.2008.4735695},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-60749133774\&partnerID=40\&md5=71ab60c63c96799064ca0bbb23fb7656},
	author = {Ascani, A. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
