A sparsity-aware QR decomposition algorithm for efficient cooperative localization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper focuses on reducing the computational complexity of the extended Kalman filter (EKF)-based multi-robot cooperative localization (CL) by taking advantage of the sparse structure of the measurement Jacobian matrix H. In contrast to the standard EKF update, whose complexity is up to O(N4) (N is the number of robots in a team), we introduce a Modified Householder QR algorithm which fully exploits the sparse structure of the matrix H, and prove that the overall complexity of the EKF update, based on our QR factorization scheme, reduces to O(N3). Finally, we validate the Modified Householder QR algorithm through extensive simulations, and demonstrate its superior performance both in terms of accuracy and CPU runtime, as compared to the current state-of-the-art QR decomposition algorithm for sparse matrices.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages799-806
Number of pages8
ISBN (Print)9781467314039
DOIs
StatePublished - Jan 1 2012
Event 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 - Saint Paul, MN, United States
Duration: May 14 2012May 18 2012

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
CountryUnited States
CitySaint Paul, MN
Period5/14/125/18/12

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