Convergence and consistency analysis for extended Kalman filter based SLAM

Huang, S; Dissanayake, G

Convergence and consistency analysis for extended Kalman filter based SLAM

Huang, S

Dissanayake, G

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2007, 23 (5), pp. 1036 - 1049
Issue Date:: 2007-10-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (386.12 kB)

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.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2007-10-01	en_US
dc.identifier.citation	IEEE Transactions on Robotics, 2007, 23 (5), pp. 1036 - 1049	en_US
dc.identifier.issn	1552-3098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5819
dc.description.abstract	This paper investigates the convergence properties and consistency of Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and- bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided. © 2007 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Robotics	en_US
dc.relation.isbasedon	10.1109/TRO.2007.903811	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Convergence and consistency analysis for extended Kalman filter based SLAM	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	23	en_US
utslib.for	091303 Autonomous Vehicles	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	ISI:000250177900020	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	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

This paper investigates the convergence properties and consistency of Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and- bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided. © 2007 IEEE.

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

http://hdl.handle.net/10453/5819