A new feature parametrization for monocular SLAM using line features

Zhao, L; Huang, S; Yan, L; Dissanayake, G

A new feature parametrization for monocular SLAM using line features

Zhao, L

Huang, S

Yan, L Dissanayake, G

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Publication Type:: Journal Article
Citation:: Robotica, 2015, 33 (3), pp. 513 - 536
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Yan, L	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.available	2020-05-25T19:19:35Z
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Robotica, 2015, 33 (3), pp. 513 - 536	en_US
dc.identifier.issn	0263-5747	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35548
dc.description.abstract	© 2014 Cambridge University Press. This paper presents a new monocular SLAM algorithm that uses straight lines extracted from images to represent the environment. A line is parametrized by two pairs of azimuth and elevation angles together with the two corresponding camera centres as anchors making the feature initialization relatively straightforward. There is no redundancy in the state vector as this is a minimal representation. A bundle adjustment (BA) algorithm that minimizes the reprojection error of the line features is developed for solving the monocular SLAM problem with only line features. A new map joining algorithm which can automatically optimize the relative scales of the local maps is used to combine the local maps generated using BA. Results from both simulations and experimental datasets are used to demonstrate the accuracy and consistency of the proposed BA and map joining algorithms.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102786
dc.relation	http://purl.org/au-research/grants/arc/LP0884112R2
dc.relation.ispartof	Robotica	en_US
dc.relation.isbasedon	10.1017/S026357471400040X	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	A new feature parametrization for monocular SLAM using line features	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	33	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
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	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 2014 Cambridge University Press. This paper presents a new monocular SLAM algorithm that uses straight lines extracted from images to represent the environment. A line is parametrized by two pairs of azimuth and elevation angles together with the two corresponding camera centres as anchors making the feature initialization relatively straightforward. There is no redundancy in the state vector as this is a minimal representation. A bundle adjustment (BA) algorithm that minimizes the reprojection error of the line features is developed for solving the monocular SLAM problem with only line features. A new map joining algorithm which can automatically optimize the relative scales of the local maps is used to combine the local maps generated using BA. Results from both simulations and experimental datasets are used to demonstrate the accuracy and consistency of the proposed BA and map joining algorithms.

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http://hdl.handle.net/10453/35548