Modeling errors in small baseline stereo for SLAM

Herath, DC; Kodagoda, KRS; Dissanayake, G

Modeling errors in small baseline stereo for SLAM

Herath, DC Kodagoda, KRS

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06, 2006
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Herath, DC	en_US
dc.contributor.author	Kodagoda, KRS 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	2006-12-01	en_US
dc.identifier.citation	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06, 2006	en_US
dc.identifier.isbn	1424403421	en_US
dc.identifier.isbn	9781424403424	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2873
dc.description.abstract	In the past few years, there has been significant advancement in localization and mapping using stereo cameras. Despite the recent successes, reliably generating an accurate geometric map of a large indoor area using stereo vision still poses significant challenges due to the accuracy and reliability of depth information especially with small baselines. Most stereo vision based applications presented to date have used medium to large baseline stereo cameras with Gaussian error models. Here we make an attempt to analyze the significance of errors in small baseline (usually <0.1m) stereo cameras and the validity of the Gaussian assumption used in the implementation of Kalman Filter based SLAM algorithms. Sensor errors are analyzed through experimentations carried out in the form of a robotic mapping. Then we show that SLAM solutions based on the Extended Kalman Filter (EKF) could become inconsistent due to the nature of the observation models used. © 2006 IEEE.	en_US
dc.relation.ispartof	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06	en_US
dc.relation.isbasedon	10.1109/ICARCV.2006.345368	en_US
dc.title	Modeling errors in small baseline stereo for SLAM	en_US
dc.type	Conference Proceeding
utslib.for	0909 Geomatic Engineering	en_US
dc.location.activity	Singapore	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.publication-status	Published	en_US

Abstract:

In the past few years, there has been significant advancement in localization and mapping using stereo cameras. Despite the recent successes, reliably generating an accurate geometric map of a large indoor area using stereo vision still poses significant challenges due to the accuracy and reliability of depth information especially with small baselines. Most stereo vision based applications presented to date have used medium to large baseline stereo cameras with Gaussian error models. Here we make an attempt to analyze the significance of errors in small baseline (usually <0.1m) stereo cameras and the validity of the Gaussian assumption used in the implementation of Kalman Filter based SLAM algorithms. Sensor errors are analyzed through experimentations carried out in the form of a robotic mapping. Then we show that SLAM solutions based on the Extended Kalman Filter (EKF) could become inconsistent due to the nature of the observation models used. © 2006 IEEE.

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http://hdl.handle.net/10453/2873