Active SLAM in structured environments

Leung, C; Huang, S; Dissanayake, G

Active SLAM in structured environments

Leung, C Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2008, pp. 1898 - 1903
Issue Date:: 2008-09-18

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Field	Value	Language
dc.contributor.author	Leung, C	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	2008-09-18	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2008, pp. 1898 - 1903	en_US
dc.identifier.isbn	9781424416479	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11075
dc.description.abstract	This paper considers the trajectory planning problem for line-feature based SLAM in structured indoor environments. The robot poses and line features are estimated using Smooth and Mapping (SAM) which is found to provide more consistent estimates than the Extended Kalman Filter (EKF). The objective of trajectory planning is to minimise the uncertainty of the estimates and to maximise coverage. Trajectory planning is performed using Model Predictive Control (MPC) with an attractor incorporating long term goals. This planning is demonstrated both in simulation and in a real-time experiment with a Pioneer2DX robot. ©2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ROBOT.2008.4543484	en_US
dc.title	Active SLAM in structured environments	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, USA	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 considers the trajectory planning problem for line-feature based SLAM in structured indoor environments. The robot poses and line features are estimated using Smooth and Mapping (SAM) which is found to provide more consistent estimates than the Extended Kalman Filter (EKF). The objective of trajectory planning is to minimise the uncertainty of the estimates and to maximise coverage. Trajectory planning is performed using Model Predictive Control (MPC) with an attractor incorporating long term goals. This planning is demonstrated both in simulation and in a real-time experiment with a Pioneer2DX robot. ©2008 IEEE.

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http://hdl.handle.net/10453/11075