Prof. Domenico Giorgio Sorrenti

In this paper, we present a novel probabilistic technique, based on the Bayes filter, able to estimate the user location, even with unreliable sensor data coming only from fixed sensors in the monitored environment. Our approach has been extensively tested in a home-like environment, as well as in a real home, and achieves very good results. We present results on two datasets, representative of real life conditions, collected during the testing phase. We detect the patient location with subroom accuracy, an improvement over the state of the art for localization using only environmental sensors. The main drawback is that it is only suitable for applications where a single person is present in the environment, like as with other approaches that do not use any mobile device. For this reason, we introduced the “telehomecare” term, therefore differentiating from generic telemedicine applications, where many people can be in the same environment at the same time.

22 June 2022 in Data in Brief

Journal Article

GTASynth: 3D synthetic data of outdoor non-urban environments.

Giovanni Curnis

Simone Fontana

Domenico Giorgio Sorrenti

Developing point clouds registration, SLAM or place recognition algorithms requires data with a high quality ground truth (usually composed of a position and orientation). Moreover, many machine learning algorithms require large amounts of data for training. However, acquiring this kind of data in non-urban outdoor environments poses several challenges. First of all, off-road robots are usually very expensive. Above all, producing an accurate ground truth is problematic. Even the best sensor available, i.e. RTK GPS, cannot guarantee the required accuracy in every condition. Hence the scarcity of this kind of dataset for point clouds registration or SLAM in off-road conditions.

8 December 2013 in Machine Vision and Applications

Journal Article

Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

Andrea Romanoni

Matteo Matteucci

Domenico Giorgio Sorrenti

9 August 2021 in Sensors

Journal Article

Vehicle Localization Using 3D Building Models and Point Cloud Matching

Augusto Luis Ballardini

Simone Fontana

Daniele Cattaneo

Matteo Matteucci

Domenico Giorgio Sorrenti

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

9 November 2017 in The European Physical Journal C

Journal Article

MOSCAB: a geyser-concept bubble chamber to be used in a dark matter search

The MOSCAB Collaboration:

A. Antonicci

Miguel Ardid

R. Bertoni

G. Bruno

L. Zanotti

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The MOSCAB experiment (Materia OSCura A Bolle) uses the “geyser technique”, a variant of the superheated liquid technique of extreme simplicity. Operating principles of the new dark matter detector an

About Me

Domenico Giorgio Sorrenti (M’89) received the maturità classica (high school degree, with literature and philosophy orientation) from Liceo G. Carducci, Milan, Italy, in 1981. He received a Laurea (master) in electronic engineering in 1989, and a Ph.D. degree in computer and control engineering in 1992, both from Politecnico di Milano, Milan. Since 1995 he has been an Assistant Professor in computer science with Università di Milano, Milan. In 1999 he moved to University of Milano - Bicocca, Milan, where he started a robotic perception laboratory. Since 2005 he has been there as Associate Professor in computer engineering. He currently teaches computer architecture and embedded systems, machine perception, and robotics for the laurea (bachelor), the laurea magistrale (master), and the Ph.D. programmes in Computer Science. His research interests are in robotic perception for autonomous vehicles. Prof. Sorrenti is a member of IEEE, Robotics and Automation, and Intelligent Transport Systems societies, and of the International Association for Pattern Recognition.

Prof. Domenico Giorgio Sorrenti

Prof. Domenico Giorgio Sorrenti

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