Efficient two step optimization for large embedded deformation graph based SLAM

Song, J; Bai, F; Zhao, L; Huang, S; Xiong, R

Efficient two step optimization for large embedded deformation graph based SLAM

Song, J Bai, F Zhao, L

Huang, S

Xiong, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2020, 00, pp. 9419-9425
Issue Date:: 2020-05-01

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Field	Value	Language
dc.contributor.author	Song, J
dc.contributor.author	Bai, F
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Xiong, R
dc.date	2020-05-31
dc.date.accessioned	2021-03-30T05:34:23Z
dc.date.available	2021-03-30T05:34:23Z
dc.date.issued	2020-05-01
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2020, 00, pp. 9419-9425
dc.identifier.isbn	9781728173955
dc.identifier.issn	1050-4729
dc.identifier.uri	http://hdl.handle.net/10453/147675
dc.description.abstract	© 2020 IEEE. Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the processing time grows rapidly in accordance with the expansion of maps. In this paper, we propose an approach to decouple the nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant. We observe that only partial deformable nodes in the graph are connected to visible points. Based on this fact, the sparsity of the original Hessian matrix is utilized to split the parameter estimation into two independent steps. With this new technique, we achieve faster parameter estimation with amortized computation complexity reduced from O(n2) to almost O(1). As a result, the computational cost barely increases as the map keeps growing. Based on our strategy, the computational bottleneck in large scale embedded deformation graph based applications will be greatly mitigated. The effectiveness is validated by experiments, featuring large scale deformation scenarios.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP200100982
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation
dc.relation.ispartof	2020 IEEE International Conference on Robotics and Automation (ICRA)
dc.relation.isbasedon	10.1109/ICRA40945.2020.9196930
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Efficient two step optimization for large embedded deformation graph based SLAM
dc.type	Conference Proceeding
utslib.citation.volume	00
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-09-15T00:00:00+1000Z
dc.date.updated	2021-03-30T05:34:21Z
pubs.finish-date	2020-08-31
pubs.publication-status	Published
pubs.start-date	2020-05-31
pubs.volume	00

Abstract:

© 2020 IEEE. Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the processing time grows rapidly in accordance with the expansion of maps. In this paper, we propose an approach to decouple the nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant. We observe that only partial deformable nodes in the graph are connected to visible points. Based on this fact, the sparsity of the original Hessian matrix is utilized to split the parameter estimation into two independent steps. With this new technique, we achieve faster parameter estimation with amortized computation complexity reduced from O(n2) to almost O(1). As a result, the computational cost barely increases as the map keeps growing. Based on our strategy, the computational bottleneck in large scale embedded deformation graph based applications will be greatly mitigated. The effectiveness is validated by experiments, featuring large scale deformation scenarios.

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http://hdl.handle.net/10453/147675