An extended Kalman filter for localisation in occupancy grid maps

Dantanarayana, L; Dissanayake, G; Ranasinghe, R; Furukawa, T

An extended Kalman filter for localisation in occupancy grid maps

Dantanarayana, L

Dissanayake, G

Ranasinghe, R

Furukawa, T

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Publication Type:: Conference Proceeding
Citation:: 2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015 - Conference Proceedings, 2016, pp. 419 - 424
Issue Date:: 2016-02-03

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Field	Value	Language
dc.contributor.author	Dantanarayana, L https://orcid.org/0000-0002-8887-329X	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Furukawa, T	en_US
dc.date.issued	2016-02-03	en_US
dc.identifier.citation	2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015 - Conference Proceedings, 2016, pp. 419 - 424	en_US
dc.identifier.isbn	9781479918768	en_US
dc.identifier.uri	http://hdl.handle.net/10453/42247
dc.description.abstract	© 2015 IEEE. The main contribution of this paper is an extended Kalman filter (EKF) based framework for mobile robot localisation in occupancy grid maps (OGMs), when the initial location is approximately known. We propose that the observation equation be formulated using the unsigned distance transform based Chamfer Distance (CD) that corresponds to a laser scan placed within the OGM, as a constraint. This formulation provides an alternative to the ray-casting model, which generally limited localisation in OGMs to Particle Filter (PF) based frameworks that can efficiently deal with observation models that are not analytic. Usage of an EKF is attractive due to its computational efficiency, especially as it can be applied to modern day field robots with limited on-board computing power. Furthermore, well-developed tools for dealing with potential outliers in the observations or changes to the motion model, exists in the EKF framework. The effectiveness of the proposed algorithm is demonstrated using a number of simulation and real life examples, including one in a dynamic environment populated with people.	en_US
dc.relation.ispartof	2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/ICIINFS.2015.7399048	en_US
dc.title	An extended Kalman filter for localisation in occupancy grid maps	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	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.publication-status	Published	en_US

Abstract:

© 2015 IEEE. The main contribution of this paper is an extended Kalman filter (EKF) based framework for mobile robot localisation in occupancy grid maps (OGMs), when the initial location is approximately known. We propose that the observation equation be formulated using the unsigned distance transform based Chamfer Distance (CD) that corresponds to a laser scan placed within the OGM, as a constraint. This formulation provides an alternative to the ray-casting model, which generally limited localisation in OGMs to Particle Filter (PF) based frameworks that can efficiently deal with observation models that are not analytic. Usage of an EKF is attractive due to its computational efficiency, especially as it can be applied to modern day field robots with limited on-board computing power. Furthermore, well-developed tools for dealing with potential outliers in the observations or changes to the motion model, exists in the EKF framework. The effectiveness of the proposed algorithm is demonstrated using a number of simulation and real life examples, including one in a dynamic environment populated with people.

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http://hdl.handle.net/10453/42247