Multi-robot simultaneous localization and mapping using D-SLAM framework

Wang, Z; Huang, S; Dissanayake, G

Multi-robot simultaneous localization and mapping using D-SLAM framework

Wang, Z Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, 2007, pp. 317 - 322
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Wang, Z	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	2007-12-01	en_US
dc.identifier.citation	Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, 2007, pp. 317 - 322	en_US
dc.identifier.isbn	1424415020	en_US
dc.identifier.isbn	9781424415021	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3172
dc.description.abstract	This paper presents an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem with the robot initial locations completely unknown. Each robot builds its own local map using the traditional Extended Kalman Filter (EKF) SLAM algorithm. We provide a new method to fuse the local maps into a jointly maintained global map by first transforming the local map state estimate into relative location information and then conducting the fusion using the decoupled SLAM (D-SLAM) framework [1O]. An efficient algorithm to find the map overlap and corresponding beacons across the maps is developed from a point feature based medical image registration method and the joint compatibility test. By adding the robot initial pose of each local map into the global map state, the algorithm shows valuable properties. Simulation results are provided to illustrate the effectiveness of the algorithm. ©2007 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP	en_US
dc.relation.isbasedon	10.1109/ISSNIP.2007.4496863	en_US
dc.title	Multi-robot simultaneous localization and mapping using D-SLAM framework	en_US
dc.type	Conference Proceeding
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Melbourne	en_US
dc.location.activity	Adelaide, AUSTRALIA
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 presents an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem with the robot initial locations completely unknown. Each robot builds its own local map using the traditional Extended Kalman Filter (EKF) SLAM algorithm. We provide a new method to fuse the local maps into a jointly maintained global map by first transforming the local map state estimate into relative location information and then conducting the fusion using the decoupled SLAM (D-SLAM) framework [1O]. An efficient algorithm to find the map overlap and corresponding beacons across the maps is developed from a point feature based medical image registration method and the joint compatibility test. By adding the robot initial pose of each local map into the global map state, the algorithm shows valuable properties. Simulation results are provided to illustrate the effectiveness of the algorithm. ©2007 IEEE.

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