Eliminating Scale Drift in Monocular SLAM Using Depth from Defocus

Shiozaki, T; Dissanayake, G

Eliminating Scale Drift in Monocular SLAM Using Depth from Defocus

Shiozaki, T

Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2018, 3 (1), pp. 581 - 587
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Shiozaki, T https://orcid.org/0000-0002-5892-8237	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.available	2017-10-24	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IEEE Robotics and Automation Letters, 2018, 3 (1), pp. 581 - 587	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120189
dc.description.abstract	© 2017 IEEE. This letter presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.	en_US
dc.relation.ispartof	IEEE Robotics and Automation Letters	en_US
dc.relation.isbasedon	10.1109/LRA.2017.2772330	en_US
dc.title	Eliminating Scale Drift in Monocular SLAM Using Depth from Defocus	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	3	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0913 Mechanical Engineering	en_US
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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

© 2017 IEEE. This letter presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.

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http://hdl.handle.net/10453/120189