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2009
IEEE

Novelty detection and 3D shape retrieval based on Gaussian Mixture Models for autonomous surveillance robotics

8 years 12 months ago
Novelty detection and 3D shape retrieval based on Gaussian Mixture Models for autonomous surveillance robotics
Abstract— This paper describes an efficient method for retrieving the 3-dimensional shape associated to novelties in the environment of an autonomous robot, which is equipped with a laser range finder. First, changes are detected over the point clouds using a combination of the Gaussian Mixture Model (GMM) and the Earth Mover’s Distance (EMD) algorithms. Next, the shape retrieval is achieved using two different algorithms. First, new samplings are generated from each Gaussian function, followed by a Random Sampling Consensus (RANSAC) algorithm to retrieve geometric primitives. Furthermore, a new algorithm is developed to directly retrieve the shape according to the mathematical space of Gaussian mixture. In this paper, the set of geometric primitives has been limited to the set C = {sphere, cylinder, plane}. The two shape retrieval methods are compared in terms of computational cost and accuracy. Experimental results in various real and simulated scenarios demonstrate the feasibi...
Pedro Núñez Trujillo, Paulo Drews, R
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where IROS
Authors Pedro Núñez Trujillo, Paulo Drews, Rui Rocha, Mario Fernando Montenegro Campos, Jorge Dias
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