Exact state and covariance sub-matrix recovery for submap based sparse EIF SLAM algorithm

Huang, S; Wang, Z; Dissanayake, G

Exact state and covariance sub-matrix recovery for submap based sparse EIF SLAM algorithm

Huang, S

Wang, Z Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2008, pp. 1868 - 1873
Issue Date:: 2008-09-18

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Field	Value	Language
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2008-09-18	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2008, pp. 1868 - 1873	en_US
dc.identifier.isbn	9781424416479	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11074
dc.description.abstract	This paper provides a novel state vector and covariance sub-matrix recovery algorithm for a recently developed submap based exactly sparse Extended Information Filter (EIF) SLAM algorithm - Sparse Local Submap Joining Filter (SLSJF). The algorithm achieves exact recovery instead of approximate recovery. The recovery algorithm is very efficient because of an incremental Cholesky factorization approach and a natural reordering of the global state vector. Simulation results show that the computation cost of the SLSJF is much lower as compared with the sequential map joining algorithm using Extended Kalman Filter (EKF). The SLSJF with the proposed recovery algorithm is also successfully applied to the Victoria Park data set. ©2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ROBOT.2008.4543479	en_US
dc.title	Exact state and covariance sub-matrix recovery for submap based sparse EIF SLAM algorithm	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Pasadena, California	en_US
dc.location.activity	Pasadena, CA
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.publication-status	Published	en_US

Abstract:

This paper provides a novel state vector and covariance sub-matrix recovery algorithm for a recently developed submap based exactly sparse Extended Information Filter (EIF) SLAM algorithm - Sparse Local Submap Joining Filter (SLSJF). The algorithm achieves exact recovery instead of approximate recovery. The recovery algorithm is very efficient because of an incremental Cholesky factorization approach and a natural reordering of the global state vector. Simulation results show that the computation cost of the SLSJF is much lower as compared with the sequential map joining algorithm using Extended Kalman Filter (EKF). The SLSJF with the proposed recovery algorithm is also successfully applied to the Victoria Park data set. ©2008 IEEE.

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