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

Esha D. Nerurkar, Stergios Roumeliotis

Research output: Contribution to journalArticlepeer-review

21 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, called the Power-SLAM, is based on three key ideas. Firstly, by introducing the Global Map Postponement method, approximations necessary for ensuring linear computational complexity of EKF-based SLAM are delayed over multiple time steps. Then by employing the PowerMethod, only the most informative of the Kalman vectors, generated during the postponement phase, are retained for updating the covariance matrix. This ensures that the information loss during each approximation epoch is minimized. Next, linear-complexity, rank-2 updates, that minimize the trace of the covariance matrix, are employed to increase the speed of convergence of the estimator. The resulting estimator, in addition to being conservative as compared to the standard EKF, has processing requirements that can be adjusted depending on the availability of computational resources. Lastly, simulation and experimental results are presented that demonstrate the accuracy of the proposed algorithm (Power-SLAM) when compared to the standard EKF-based SLAM with quadratic computational cost and two linear-complexity competing alternatives.

Original languageEnglish (US)
Pages (from-to)772-788
Number of pages17
JournalInternational Journal of Robotics Research
Volume30
Issue number6
DOIs
StatePublished - May 1 2011

Keywords

  • Global map postponement
  • Power method
  • Simultaneous localization and mapping

Fingerprint Dive into the research topics of 'Power-SLAM: A linear-complexity, anytime algorithm for SLAM'. Together they form a unique fingerprint.

Cite this