Publications

http://hdl.handle.net/1853/38196

Authors: 107 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA; eng 2-s2.0-32644444569

Journal: United States||Italy||United States

Published: false

DOI: Georgia Institute of Technology||University of Padua||Georgia Institute of Technology

Volume: Ranganathan A.; Menegatti E.; Dellaert F. Pages: Ranganathan||Menegatti||Dellaert-While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding general-purpose methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies, given available sensor measurements. We show that the space of topologies is equivalent to the intractably large space of set partitions on the set of available measurements. The combinatorial nature of the problem is overcome by computing an approximate, sample-based representation of the posterior. The PTM is obtained by performing Bayesian inference over the space of all possible topologies, and provides a systematic solution to the problem of perceptual aliasing in the domain of topological mapping. In this paper, we describe a general framework for modeling measurements, and the use of a Markov-chain Monte Carlo algorithm that uses specific instances of these models for odometry and appearance measurements to estimate the posterior distribution. We present experimental results that validate our technique and generate good maps when using odometry and appearance, derived from panoramic images, as sensor measurements. © 2006 IEEE.

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Authors: 1383 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA; eng 2-s2.0-33947211747

Journal: Italy||Italy||Italy

Published: false

DOI: University of Padua||Udine University||University of Padua

Volume: Pagello E.; D'Angelo A.; Menegatti E. Pages: Pagello||D'Angelo||Menegatti-This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots. © 2006 IEEE.

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Authors: 535 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA; eng 2-s2.0-33745177290

Journal: Italy||Italy||Italy||Italy

Published: false

DOI: University of Padua||University of Padua||University of Padua||CNR Padua

Volume: Menegatti E.; Pretto A.; Scarpa A.; Pagello E. Pages: Menegatti||Pretto||Scarpa||Pagello-The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware. © 2006 IEEE.

scopus.description.abstract||scopus.subject.keywords||scopus.description.allpeopleoriginal

Authors: 535 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA; eng 2-s2.0-33745177290

Journal: Italy||Italy||Italy||Italy

Published: false

DOI: University of Padua||University of Padua||University of Padua||CNR Padua

Volume: Menegatti E.; Pretto A.; Scarpa A.; Pagello E. Pages: Menegatti||Pretto||Scarpa||Pagello-The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware. © 2006 IEEE.

W3009560954

Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione; 2015-06-05T01:02:04Z

Published: title_year

W2613733732

Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione; Non assegn; 2015-06-05T01:02:04Z

Published: title_year

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Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione; Non assegn; ITA; 2015-06-05T02:59:42Z; 9788890042621

Published: MATCH_RESULT_STATUS_FAILURE_NO_MATCH

W2137030978

Authors: Non assegn; AREA MIN. 09 - Ingegneria industriale e dell'informazione; 2015-06-05T02:55:28Z; 9788890042621

Published: title_year

scopus.description.allpeopleoriginal

Authors: 232 NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS; eng 2-s2.0-34548256473

Journal: Italy||Italy||Italy||Italy||Italy||Japan||Italy

Published: 18

DOI: University of Padua||University of Padua||University of Padua||National Research Council||National Research Council||Osaka University||Institute of Biomedical Engineering of the National Research Council (ISIB-CNR)

Volume: Menegatti E.; Simionato C.; Tonello S.; Cicirelli G.; Distante A.; Ishiguro H.; Pagello E. Pages: Menegatti||Simionato||Tonello||Cicirelli||Distante||Ishiguro||Pagello-In this paper an omnidirectional Distributed Vision System (DVS) is presented. The presented DVS is able to learn to navigate a mobile robot in its working environment without any prior knowledge about calibration parameters of the cameras or the control law of the robot (this is an important feature if we want to apply this system to existing camera networks). The DVS consists of different Vision Agents (VAs) implemented by omnidirectional cameras. The main contribution of the work is the explicit distribution of the acquired knowledge in the DVS. The aim is to develop a totally autonomous system able not only to learn control policies by on-line learning, but also to deal with a changing environment and to improve its performance during lifetime. Once an initial knowledge is acquired by one Vision Agent, this knowledge can be transferred to other Vision Agents in order to exploit what was already learned. In this paper, first we investigate how the Vision Agent learns the knowledge, then we evaluate its performance and test the knowledge propagation on three different VAs. Experiments are reported both using a system simulator and using a prototype of the Distributed Vision System in a real environment demonstrating the feasibility of the approach.

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Authors: 518 HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY; eng 2-s2.0-38049173172

Journal: Italy||Italy||Italy||Italy

Published: false

DOI: Department of Information Engineering||Department of Information Engineering||Department of Information Engineering||Department of Information Engineering

Volume: Lastra A.; Pretto A.; Tonello S.; Menegatti E. Pages: Lastra||Pretto||Tonello||Menegatti-Detection of human skin in an arbitrary image is generally hard. Most color-based skin detection algorithms are based on a static color model of the skin. However, a static model cannot cope with the huge variability of scenes, illuminants and skin types. This is not suitable for an interacting robot that has to find people in different rooms with its camera and without any a priori knowledge about the environment nor of the lighting. In this paper we present a new color-based algorithm called VR filter. The core of the algorithm is based on a statistical model of the colors of the pixels that generates a dynamic boundary for the skin pixels in the color space. The motivation beyond the development of the algorithm was to be able to correctly classify skin pixels in low definition images with moving objects, as the images grabbed by the omnidirectional camera mounted on the robot. However, our algorithm was designed to correctly recognizes skin pixels with any type of camera and without exploiting any information on the camera. In the paper we present the advantages and the limitations of our: algorithm and we compare its performances with the principal existing skin detection algorithms on standard perspective images. © Springer-Verlag Berlin Heidelberg 2007.