Novelty detection and 3D shape retrieval based on Gaussian mixture models for autonomous surveillance robotics

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

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 C = {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.

Original languageBritish English
Title of host publication2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Pages4724-4730
Number of pages7
DOIs
StatePublished - 11 Dec 2009
Event2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO, United States
Duration: 11 Oct 200915 Oct 2009

Publication series

Name2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

Conference

Conference2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Country/TerritoryUnited States
CitySt. Louis, MO
Period11/10/0915/10/09

Fingerprint

Dive into the research topics of 'Novelty detection and 3D shape retrieval based on Gaussian mixture models for autonomous surveillance robotics'. Together they form a unique fingerprint.

Cite this