A review of optimisation strategies used in simultaneous localisation and mapping

Huang, S

A review of optimisation strategies used in simultaneous localisation and mapping

Huang, S

Permalink

Publication Type:: Journal Article
Citation:: Journal of Control and Decision, 2019, 6 (1), pp. 61 - 74
Issue Date:: 2019-01-02

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (1.1 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.available	2020-05-25T19:05:11Z
dc.date.issued	2019-01-02	en_US
dc.identifier.citation	Journal of Control and Decision, 2019, 6 (1), pp. 61 - 74	en_US
dc.identifier.issn	2330-7706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130036
dc.description.abstract	© 2018, © 2018 Northeastern University, China. This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping (SLAM) problem. The focus is on the optimisation-based SLAM back end. The strategies are classified based on their purposes such as reducing the computational complexity, improving the convergence and improving the robustness. It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution. The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end. Although more research is needed to further improve the SLAM back end, nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.	en_US
dc.relation.ispartof	Journal of Control and Decision	en_US
dc.relation.isbasedon	10.1080/23307706.2018.1552207	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A review of optimisation strategies used in simultaneous localisation and mapping	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	6	en_US
utslib.for	0803 Computer Software	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	open_access	*
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2018, © 2018 Northeastern University, China. This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping (SLAM) problem. The focus is on the optimisation-based SLAM back end. The strategies are classified based on their purposes such as reducing the computational complexity, improving the convergence and improving the robustness. It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution. The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end. Although more research is needed to further improve the SLAM back end, nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.

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

http://hdl.handle.net/10453/130036