Detecção de Mudanças de Forma em Mapas 3D Baseados em Nuvens de Pontos

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

In Proc. of XVIII Congresso Brasileiro de Automática, Bonito, Brazil, pages 2086-2092, Sep. 12-16, 2010. (In Portuguese)

Abstract

There are several applications for which it is important to both detect and modelling changes. In the context of 3D point cloud, detecting changes constitutes a challenging task due to the large amounts of data that must be compared, as well as shape retrieval needs to deal with large kind of shape and with noise in the data. Thus, this paper proposes a framework to efficiently detect and represent changes through a compact model. Initially, the point cloud is simplified in order to preserve the geometric characteristics. Moreover, a combined approach using Gaussian Mixture Models (GMM) and the Earth Mover's Distance (EMD) is proposed, together with a greedy technique. It allows detect and segment changes, through previous knowledge about the environment map. The 3D shape retrieval is achieved using two different approaches. The first approach is a new method that we developed which directly retrieves basic shapes the detected shape in the Gaussian space (planes, spheres and cylinders). The second approach uses superquadric models and the split-and-merge paradigm due to their good expressiveness in representing 3D shapes. Experimental results in various real scenarios were obtained to evaluate the approach's feasibility and robustness with large datasets.

Index Terms — Novelty detection, 3D maps.

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BibTeX

@INPROCEEDINGS(Drews_et_al_10b,

AUTHOR = "Drews Jr, P. and Rocha, R. and Campos, M. and Dias, J.",

TITLE = "Detecção de Mudanças de Forma em Mapas 3\textsc{D} Baseados em Nuvens de Pontos",

BOOKTITLE = "Proc. of XVIII Congresso Brasileiro de Automática",

ADDRESS = "Bonito, Brazil",

YEAR = "2010",

MONTH = "Sep.",

PAGES = "2086-2092"

)

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Last update: 18/10/2010