An Analysis of the bias correction problem in simultaneous localization and mapping

Wijesoma, WS; Perera, L; Adams, MD; Challa, S

An Analysis of the bias correction problem in simultaneous localization and mapping

Wijesoma, WS Perera, L Adams, MD Challa, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005 (IROS 2005), 2005, pp. 747 - 752
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Wijesoma, WS	en_US
dc.contributor.author	Perera, L	en_US
dc.contributor.author	Adams, MD	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.editor	N/A	en_US
dc.date	2005-08-02	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005 (IROS 2005), 2005, pp. 747 - 752	en_US
dc.identifier.isbn	0-7803-8912-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2882
dc.description.abstract	Unmodeled systematic and nonsystematic errors in robot kinematics and measurement processes often cause adverse effects in several autonomous navigation tasks. In particular, accumulated sensor biases can render simultaneous localization and mapping (SLAM) algorithms of autonomous vehicles to perform very poorly especially in large unexplored terrains including cycles, as a result of the estimator divergence and inconsistency. One way to deal with this problem is the accurate modeling and precise calibration of sensors. However this may add up to longer setup and calibration times. Even after accurate calibration and modeling, sensor calibration may often subject to drifts, rendering the efforts ineffective. Therefore, the correct and effective way to deal with this problem is explicit estimation of these parameters with other states. In this work we address the estimation theoretic sensor bias correction problem in SLAM using a simple unified framework and establish theoretically, the behavior and properties of the solution with special consideration to diminishing uncertainty, rates of convergence and observability	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005 (IROS 2005)	en_US
dc.relation.ispartof	IEEE/RSJ International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2005.1544952	en_US
dc.rights	© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	An Analysis of the bias correction problem in simultaneous localization and mapping	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Piscataway, USA	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0910 Manufacturing Engineering	en_US
dc.location.activity	Piscataway, USA	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
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2005-08-02	en_US

Abstract:

Unmodeled systematic and nonsystematic errors in robot kinematics and measurement processes often cause adverse effects in several autonomous navigation tasks. In particular, accumulated sensor biases can render simultaneous localization and mapping (SLAM) algorithms of autonomous vehicles to perform very poorly especially in large unexplored terrains including cycles, as a result of the estimator divergence and inconsistency. One way to deal with this problem is the accurate modeling and precise calibration of sensors. However this may add up to longer setup and calibration times. Even after accurate calibration and modeling, sensor calibration may often subject to drifts, rendering the efforts ineffective. Therefore, the correct and effective way to deal with this problem is explicit estimation of these parameters with other states. In this work we address the estimation theoretic sensor bias correction problem in SLAM using a simple unified framework and establish theoretically, the behavior and properties of the solution with special consideration to diminishing uncertainty, rates of convergence and observability

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http://hdl.handle.net/10453/2882