Feature based SLAM using laser sensor data with maximized information usage

Liu, M; Huang, S; Dissanayake, G

Feature based SLAM using laser sensor data with maximized information usage

Liu, M Huang, S

Dissanayake, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2011, pp. 1811 - 1816
Issue Date:: 2011-12-01

Closed Access

	Filename	Description	Size
	2010006675OK.pdf	Published version	3.16 MB	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	Liu, M	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	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2011, pp. 1811 - 1816	en_US
dc.identifier.isbn	9781612843865	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32362
dc.description.abstract	This paper formulates the SLAM problem using 2D laser data as an optimization problem. The environment is modeled as a set of curves and the variables of the optimization problem are the robot poses as well as the parameters describing the curves. There are two key differences between this SLAM formulation and existing SLAM methods. First, the environment is represented by continuous curves instead of point clouds or occupancy grids. Second, all the laser readings, including laser beams which returns its maximum range value, have been included in the objective function. As the objective function to be optimized contains discontinuities, it can not be solved by standard gradient based approaches and thus a Genetic Algorithm (GA) based method is applied. Matching of laser scans acquired from relatively far apart robot poses is achieved by applying GA on top of the Iterative closest point (ICP) algorithm. The new SLAM formulation and the use of a global optimization algorithm successfully avoid the convergence to local minimum for both the scan matching and the SLAM problem. Both simulated and experimental data are used to demonstrate the effectiveness of the proposed techniques. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2011.5980504	en_US
dc.title	Feature based SLAM using laser sensor data with maximized information usage	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Shanghai, PEOPLES R CHINA
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.publication-status	Published	en_US

Abstract:

This paper formulates the SLAM problem using 2D laser data as an optimization problem. The environment is modeled as a set of curves and the variables of the optimization problem are the robot poses as well as the parameters describing the curves. There are two key differences between this SLAM formulation and existing SLAM methods. First, the environment is represented by continuous curves instead of point clouds or occupancy grids. Second, all the laser readings, including laser beams which returns its maximum range value, have been included in the objective function. As the objective function to be optimized contains discontinuities, it can not be solved by standard gradient based approaches and thus a Genetic Algorithm (GA) based method is applied. Matching of laser scans acquired from relatively far apart robot poses is achieved by applying GA on top of the Iterative closest point (ICP) algorithm. The new SLAM formulation and the use of a global optimization algorithm successfully avoid the convergence to local minimum for both the scan matching and the SLAM problem. Both simulated and experimental data are used to demonstrate the effectiveness of the proposed techniques. © 2011 IEEE.

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

http://hdl.handle.net/10453/32362