A two-tier map representation for compact-stereo-vision-based SLAM

Herath, DC; Kodagoda, S; Dissanayake, G

A two-tier map representation for compact-stereo-vision-based SLAM

Herath, DC

Kodagoda, S

Dissanayake, G

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Publication Type:: Journal Article
Citation:: Robotica, 2012, 30 (2), pp. 245 - 256
Issue Date:: 2012-03-01

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Field	Value	Language
dc.contributor.author	Herath, DC https://orcid.org/0000-0002-7509-5265	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2012-03-01	en_US
dc.identifier.citation	Robotica, 2012, 30 (2), pp. 245 - 256	en_US
dc.identifier.issn	0263-5747	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22344
dc.description.abstract	Vision sensors are increasingly being used in the implementation of Simultaneous Localization and Mapping (SLAM). Even though the mathematical framework of SLAM is well understood, considerable issues remain to be resolved when a particular sensing modality is considered. For instance, the observation model of a small baseline stereo camera is known to be highly nonlinear. As a consequence, state estimations obtained from standard recursive estimators, such as the Extended Kalman Filter, tend to be inconsistent. Further, vision-based approaches are plagued with high feature densities, and the consequent requisite of maintaining large feature databases for loop closure and data association. This paper proposes a two-tier solution for resolving these issues, inspired by the mechanics of human navigation. The proposed two-tier solution addresses the consistency issue by formulating the SLAM problem as a nonlinear batch optimization and presents a novel method for feature management through a two-tier map representation. Simulations and experiments are carried out in an office-like environment to validate the performance of the algorithm. © Copyright Cambridge University Press 2011.	en_US
dc.relation.ispartof	Robotica	en_US
dc.relation.isbasedon	10.1017/S0263574711000543	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	A two-tier map representation for compact-stereo-vision-based SLAM	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	30	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	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

Vision sensors are increasingly being used in the implementation of Simultaneous Localization and Mapping (SLAM). Even though the mathematical framework of SLAM is well understood, considerable issues remain to be resolved when a particular sensing modality is considered. For instance, the observation model of a small baseline stereo camera is known to be highly nonlinear. As a consequence, state estimations obtained from standard recursive estimators, such as the Extended Kalman Filter, tend to be inconsistent. Further, vision-based approaches are plagued with high feature densities, and the consequent requisite of maintaining large feature databases for loop closure and data association. This paper proposes a two-tier solution for resolving these issues, inspired by the mechanics of human navigation. The proposed two-tier solution addresses the consistency issue by formulating the SLAM problem as a nonlinear batch optimization and presents a novel method for feature management through a two-tier map representation. Simulations and experiments are carried out in an office-like environment to validate the performance of the algorithm. © Copyright Cambridge University Press 2011.

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http://hdl.handle.net/10453/22344