Schmidt or Compressed filtering for Visual-Inertial SLAM?

Byun, H; Kim, J; Vanegas, F; Gonzalez, F

Schmidt or Compressed filtering for Visual-Inertial SLAM?

Byun, H Kim, J

Vanegas, F Gonzalez, F

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2021 Australasian Conference on Robotics and Automation, ACRA, 6-8 December 2021, Melbourne, Australia, pp. 197-201
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Byun, H
dc.contributor.author	Kim, J https://orcid.org/0000-0003-4505-1103
dc.contributor.author	Vanegas, F
dc.contributor.author	Gonzalez, F
dc.date.accessioned	2023-04-05T05:15:37Z
dc.date.available	2023-04-05T05:15:37Z
dc.date.issued	2021-01-01
dc.identifier.citation	Proceedings of the 2021 Australasian Conference on Robotics and Automation, ACRA, 6-8 December 2021, Melbourne, Australia, pp. 197-201
dc.identifier.isbn	9780645565508
dc.identifier.issn	1448-2053
dc.identifier.uri	http://hdl.handle.net/10453/169204
dc.description.abstract	Visual-Inertial SLAM has been studied widely due to the advantage of its lightweight, cost-effectiveness, and rich information compared to other sensors. A multi-state constrained filter (MSCKF) and its Schmidt version have been developed to address the computational cost, which treats key-frames as static nuisance parameters, leading to sub-optimal performance. We propose a new Compressed-MSCKF which can achieve improved accuracy with moderate computational costs. By keeping the information gain with compressed form, it can be limited to O(L) with L being the number of local key-frames. The performance of the proposed system has been evaluated using a MATLAB simulator.
dc.language	en
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Schmidt or Compressed filtering for Visual-Inertial SLAM?
dc.type	Conference Proceeding
utslib.citation.volume	2021-December
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
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-05T05:15:36Z
pubs.publication-status	Published
pubs.volume	2021-December

Abstract:

Visual-Inertial SLAM has been studied widely due to the advantage of its lightweight, cost-effectiveness, and rich information compared to other sensors. A multi-state constrained filter (MSCKF) and its Schmidt version have been developed to address the computational cost, which treats key-frames as static nuisance parameters, leading to sub-optimal performance. We propose a new Compressed-MSCKF which can achieve improved accuracy with moderate computational costs. By keeping the information gain with compressed form, it can be limited to O(L) with L being the number of local key-frames. The performance of the proposed system has been evaluated using a MATLAB simulator.

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http://hdl.handle.net/10453/169204