Dense 3D map construction for indoor search and rescue

Ellekilde, LP; Huang, S; Miró, JV; Dissanayake, G

Dense 3D map construction for indoor search and rescue

Ellekilde, LP Huang, S

Miró, JV

Dissanayake, G

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Publication Type:: Journal Article
Citation:: Journal of Field Robotics, 2007, 24 (1-2), pp. 71 - 89
Issue Date:: 2007-01-01

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Field	Value	Language
dc.contributor.author	Ellekilde, LP	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Miró, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2007-01-01	en_US
dc.identifier.citation	Journal of Field Robotics, 2007, 24 (1-2), pp. 71 - 89	en_US
dc.identifier.issn	1556-4959	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4229
dc.description.abstract	The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three-dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstructured indoor environments. A key challenge in this scenario is that the robot moves in 6D and no odometry information is available. An extended information filter (EIF) is used to estimate the state vector containing the sequence of camera poses and some selected 3D point features in the environment. Data association is performed using a combination of scale invariant feature transformation (SIFT) feature detection and matching, random sampling consensus (RANSAC), and least square 3D point sets fitting. Experimental results are provided to demonstrate the effectiveness of the techniques developed. © 2007 Wiley Periodicals, Inc.	en_US
dc.relation.ispartof	Journal of Field Robotics	en_US
dc.relation.isbasedon	10.1002/rob.20173	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Dense 3D map construction for indoor search and rescue	en_US
dc.type	Journal Article
utslib.citation.volume	1-2	en_US
utslib.citation.volume	24	en_US
utslib.for	091303 Autonomous Vehicles	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Sydney, Australia
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.issue	1-2	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three-dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstructured indoor environments. A key challenge in this scenario is that the robot moves in 6D and no odometry information is available. An extended information filter (EIF) is used to estimate the state vector containing the sequence of camera poses and some selected 3D point features in the environment. Data association is performed using a combination of scale invariant feature transformation (SIFT) feature detection and matching, random sampling consensus (RANSAC), and least square 3D point sets fitting. Experimental results are provided to demonstrate the effectiveness of the techniques developed. © 2007 Wiley Periodicals, Inc.

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http://hdl.handle.net/10453/4229