Observability-based consistent EKF estimators for multi-robot cooperative localization

Guoquan P. Huang, Nikolas Trawny, Anastasios I. Mourikis, Stergios I. Roumeliotis

Research output: Contribution to journalArticlepeer-review

83 Scopus citations


In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. We analytically show that the error-state system model employed in the standard EKF-based CL always has an observable subspace of higher dimension than that of the actual nonlinear CL system. This results in unjustified reduction of the EKF covariance estimates in directions of the state space where no information is available, and thus leads to inconsistency. To address this problem, we adopt an observability-based methodology for designing consistent estimators in which the linearization points are selected to ensure a linearized system model with observable subspace of correct dimension. In particular, we propose two novel observability-constrained (OC)-EKF estimators that are instances of this paradigm. In the first, termed OC-EKF 1.0, the filter Jacobians are calculated using the prior state estimates as the linearization points. In the second, termed OC-EKF 2.0, the linearization points are selected so as to minimize their expected errors (i.e., the difference between the linearization point and the true state) under the observability constraints. The proposed OC-EKFs have been tested in simulation and experimentally, and have been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.

Original languageEnglish (US)
Pages (from-to)99-122
Number of pages24
JournalAutonomous Robots
Issue number1
StatePublished - Jan 2011

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the University of Minnesota (DTC), the UCR Bourns College of Engineering, and the National Science Foundation (IIS-0835637, IIS-0643680, IIS-0811946).


  • Cooperative localization
  • Estimator inconsistency
  • Extended Kalman filter
  • Linearization errors
  • Nonlinear estimation
  • Observability


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