Novelty Detection and 3D Shape Retrieval based on Gaussian Mixture Models for Autonomous Surveillance Robotics

P. Núñez, P. Drews Jr, R. Rocha, M. Campos and J. Dias

In Proc. of 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS'2009), St. Louis, MO, USA, pages 4724-4730, Oct. 11-15, 2009

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 (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 feasibility of the approach.

Index Terms — Novelty detection, shape retrieval, Gaussian mixture models, earth mover’s distance, RANSAC.

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BibTeX

@INPROCEEDINGS(Nunez_et_al_09b,

AUTHOR = "N{\’u}{\~n}ez, P. and Drews Jr, P. and Rocha, R. and Campos, M. and Dias, J.",

TITLE = "Novelty Detection and 3\textsc{D} Shape Retrieval based on \textsc{G}aussian \textsc{M}ixture \textsc{M}odels for Autonomous Surveillance Robotics",

BOOKTITLE = "Proc. of 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS'2009)",

ADDRESS = "St. Louis, MO, USA",

YEAR = "2009",

MONTH = "Oct.",

PAGES = "4724-4730"

)

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Last update: 21/10/2009