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

Content Provider	Springer Nature Link
Author	Huang, Guoquan P. Trawny, Nikolas Mourikis, Anastasios I. Roumeliotis, Stergios I.
Copyright Year	2010
Abstract	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.
Starting Page	99
Ending Page	122
Page Count	24
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	30
Issue Number	1
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2010-09-14
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cooperative localization Nonlinear estimation Extended Kalman filter Linearization errors Estimator inconsistency Observability Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Control , Robotics, Mechatronics Robotics and Automation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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