Power-SLAM: A linear-complexity, consistent algorithm for SLAM

Esha D. Nerurkar, Stergios Roumeliotis

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

8 Scopus citations

Abstract

In this paper, we present an Extended Kalman Filter (EKF)-based estimator for simultaneous localization and mapping (SLAM) with processing requirements that are linear in the number of features in the map. The proposed algorithm is based on three key ideas. Firstly, by introducing the Global-Map Postponement method, approximations necessary for ensuring linear computational complexity are delayed over many time steps. Then by employing the Power Method, only the most informative of the Kalman vectors, generated during the postponement phase, are retained for updating the covariance matrix. This in effect minimizes the information loss during each approximation epoch. Finally, linear-complexity, rank-2 updates, which minimize the trace of the covariance matrix, are applied to increase the speed of convergence of the estimator. In addition to being consistent, the resulting estimator has processing requirements that can be adjusted to the availability of computational resources. Simulation results are presented that demonstrate the accuracy of the proposed algorithm (Power-SLAM) when compared to the quadratic computational cost standard EKF-based SLAM, and two linear-complexity competing alternatives.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages636-643
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
Duration: Oct 29 2007Nov 2 2007

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
CountryUnited States
CitySan Diego, CA
Period10/29/0711/2/07

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