Open AccessThree-Dimensional Statistical Gas Distribution Mapping in an Uncontrolled Indoor Environment
Author(s) -
Matteo Reggente,
Achim J. Lilienthal,
Matteo Pardo,
Giorgio Sberveglieri
Publication year - 2009
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.177
H-Index - 75
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.3156484
Subject(s) - extrapolation , computer science , kernel (algebra) , gaussian , mobile robot , artificial intelligence , random variate , statistical model , kernel density estimation , distribution (mathematics) , computer vision , mathematics , statistics , robot , random variable , physics , mathematical analysis , quantum mechanics , estimator , combinatorics
In this paper we present a statistical method to build three-dimensional gas distribution maps (3D-DM). The proposed mapping technique uses kernel extrapolation with a tri-variate Gaussian kernel that models the likelihood that a reading represents the concentration distribution at a distant location in the three dimensions. The method is evaluated using a mobile robot equipped with three "e-noses" mounted at different heights. Initial experiments in an uncontrolled indoor environment are presented and evaluated with respect to the ability of the 3D map, computed from the lower and upper nose, to predict the map from the middle nose
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