NDLI: Novelty detection and 3D shape retrieval using superquadrics and multi-scale sampling for autonomous mobile robots

Content Provider	IEEE Xplore Digital Library
Author	Drews, P. Núñez, P. Rocha, R. Campos, M. Dias, J.
Copyright Year	2010
Description	Author affiliation: ISIS Group, Universidad de Málaga, and Dept. Tecnología de los Computadores y las Comunicaciones, Universidad de Extremadura, Spain (Núñez, P.) \|\| Computer Science, Federal University of Minas Gerais, Brazil (Drews, P.; Campos, M.) \|\| Institute of Systems and Robotics, Dept. Electrical and Computer Engineering, University of Coimbra, Portugal (Rocha, R.; Dias, J.)
Abstract	There are several applications for which it is important to both detect and communicate changes in data models. For instance, in some mobile robotics applications (e.g. surveillance) a robot needs to detect significant changes in the environment (e.g. a layout change) which it may achieve by comparing current data provided by its sensors with previously acquired data (e.g. 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. This paper proposes a framework to detect, and represent changes through a compact model. The main steps of the procedure are: multi-scale 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.
Starting Page	3635
Ending Page	3640
File Size	2031667
Page Count	6
File Format	PDF
ISBN	9781424450381
ISSN	10504729
DOI	10.1109/ROBOT.2010.5509405
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Sampling methods Mobile robots Robot sensing systems Navigation Robotics and automation Information retrieval Data models Surveillance Simultaneous localization and mapping
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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