Abstract
In this paper, we seek to provide consistent, real-time 3D localization capabilities to mobile devices navigating within previously mapped areas. To this end, we introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which explicitly considers the uncertainty of the prior map, by employing the sparse Cholesky factor of the map's Hessian, instead of its dense covariance-as is the case for the Schmidt-Kalman filter. By doing so, the C-SKF has memory requirements typically linear in the size of the map, as opposed to quadratic for storing the map's covariance. Moreover, and in order to bound the processing needs of the C-SKF (between linear and quadratic in the size of the map), we introduce two relaxations of the C-SKF algorithm: (i) The sC-SKF, which operates on the Cholesky factors of independent sub-maps resulting from dividing the map into overlapping segments. (ii) We formulate an efficient method for sparsifying the Cholesky factor by selecting and processing a subset of loop-closure measurements based on their temporal distribution. Lastly, we assess the processing and memory requirements of the proposed algorithms, and compare their positioning accuracy against other inconsistent map-based localization approaches that employ measurement-noise-covariance inflation to compensate for the map's uncertainty.
Original language | English (US) |
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Title of host publication | ICRA 2017 - IEEE International Conference on Robotics and Automation |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6253-6260 |
Number of pages | 8 |
ISBN (Electronic) | 9781509046331 |
DOIs | |
State | Published - Jul 21 2017 |
Event | 2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore Duration: May 29 2017 → Jun 3 2017 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |
Other
Other | 2017 IEEE International Conference on Robotics and Automation, ICRA 2017 |
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Country/Territory | Singapore |
City | Singapore |
Period | 5/29/17 → 6/3/17 |
Bibliographical note
Funding Information:† R. C. Dutoit and S. I. Roumeliotis are with the Department of Computer Science, University of Minnesota {dutoit,stergios}@cs.umn.edu ‡ J. A. Hesch and E. D. Nerurkar are with Google Inc. {joelhesch, eshanerurkar}@google.com This work was supported by Google, Project Tango.
Publisher Copyright:
© 2017 IEEE.