Novelty Detection and 3D Shape Retrieval using Superquadrics and Multi-Scale Sampling for Autonomous Mobile Robots

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

In Proc. of 2010 IEEE Int. Conf. on Robotics and Automation (ICRA’2010), Anchorage, Alaska, USA, pages 3635-3640, May 3-8, 2010

Abstract

In many applications, it is important to detect changes in data models and communicate these changes efficiently. For instance, in some mobile robotics applications (e.g. surveillance) the robot needs to detect significant changes in the environment (e.g a layout change) by comparing data provided by its sensors with a previously acquired map of the environment. This feature is often an extremely challenging one because large amounts of data must be compared in real-time. This paper proposes a framework to efficiently detect and segment changes in a data model and represent these changes through a compact model. It comprises: multiscale sampling to reduce the computation burden; change detection based on Gaussian mixture models; fitting superquadrics to detected changes; and refinement and optimization using the split and merge paradigm. Experimental results in various real and simulated scenarios demonstrate the approach's feasibility and robustness with large datasets.

Index Terms — Novelty detection, shape retrieval, Gaussian mixture models, superquadrics, multiscale sampling.

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BibTeX

@INPROCEEDINGS(Drews_et_al_10,

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

TITLE = "Novelty Detection and 3D Shape Retrieval using Superquadrics and Multi-Scale Sampling for Autonomous Mobile Robots",

BOOKTITLE = "Proc. of 2010 IEEE Int. Conf. on Robotics and Automation",

ADDRESS = "Anchorage, Alaska, USA",

YEAR = "2010",

MONTH = "May",

PAGES = "3635-3640"

)

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Last update: 15/05/2010