Simultaneous Localization and Mapping: Exactly Sparse Information Filters

Wang, Z; Huang, S; Dissanayake, G

Simultaneous Localization and Mapping: Exactly Sparse Information Filters

Wang, Z Huang, S

Dissanayake, G

Permalink

Publisher:: World Scientific Publishing
Publication Type:: Book
Citation:: 2011, 1
Issue Date:: 2011-01

Closed Access

	Filename	Description	Size
	2010006665OK.pdf		813.76 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	2011, 1	en_US
dc.identifier.isbn	981-4350-31-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17712
dc.description.abstract	Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.	en_US
dc.publisher	World Scientific Publishing	en_US
dc.title	Simultaneous Localization and Mapping: Exactly Sparse Information Filters	en_US
dc.type	Book
utslib.location	Singapore	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.citation.edition	1	en_US
dc.location.activity	Como, Italy
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.consider-herdc	true	en_US
pubs.edition	1	en_US
pubs.place-of-publication	Singapore	en_US

Abstract:

Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/17712