Topological local-metric framework for mobile robots navigation: a long term perspective

Tang, L; Wang, Y; Ding, X; Yin, H; Xiong, R; Huang, S

Topological local-metric framework for mobile robots navigation: a long term perspective

Tang, L Wang, Y Ding, X Yin, H Xiong, R Huang, S

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Publication Type:: Journal Article
Citation:: Autonomous Robots, 2019, 43 (1), pp. 197 - 211
Issue Date:: 2019-01-31

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Field	Value	Language
dc.contributor.author	Tang, L	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Ding, X	en_US
dc.contributor.author	Yin, H	en_US
dc.contributor.author	Xiong, R	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.available	2020-05-25T19:08:38Z
dc.date.issued	2019-01-31	en_US
dc.identifier.citation	Autonomous Robots, 2019, 43 (1), pp. 197 - 211	en_US
dc.identifier.issn	0929-5593	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131696
dc.description.abstract	© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.	en_US
dc.relation.ispartof	Autonomous Robots	en_US
dc.relation.isbasedon	10.1007/s10514-018-9724-7	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Topological local-metric framework for mobile robots navigation: a long term perspective	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	43	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	0913 Mechanical Engineering	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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	43	en_US

Abstract:

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.

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http://hdl.handle.net/10453/131696