Change Detection in 3D Environments Based on Gaussian Mixture Model and Robust Structural Matching for Autonomous Robotic Applications

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

In Proc. of 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS'2010), Taipei, Taiwan, pages 2633-2638, Oct. 18-22, 2010.

Abstract

The ability to detect perceptions which were never experienced before, i.e. novelty detection, is an important component of autonomous robots working in real environments. It is achieved by comparing current data provided by its sensors with a previously known map of the environment. This often constitutes an extremely challenging task due to the large amounts of data that must be compared in real-time. With respect to previously proposed approaches, this paper detects changes in 3D environment based on probabilistic models, the Gaussian Mixture Model, and a fast and robust combined constraint matching algorithm. The matching allows to represent the scene view as a graph which emerges from the comparison between Mixtures of Gaussians. Finding the largest set of mutually consistent matches is equivalent to find the maximum clique on a graph. The proposed approach has been tested for mobile robotics purposes in real environments and compared to other matching algorithms. Experimental results demonstrate the performance of the proposal.

Index Terms — Novelty detection, Gaussian mixture models, constraint matching, maximum clique.

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BibTeX

@INPROCEEDINGS(Nunez_et_al_10,

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

TITLE = "Change Detection in 3\textsc{D} Environments Based on \textsc{G}aussian \textsc{M}ixture \textsc{M}odel and Robust Structural Matching for Autonomous Robotic Applications",

BOOKTITLE = "Proc. of 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS'2010)",

ADDRESS = "Taipei, Taiwan",

YEAR = "2010",

MONTH = "Oct.",

PAGES = "2633-2638"

)

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Last update: 25/10/2010