Localization of a Group of Communicating Vehicles by State Exchange

Karam, Nadir; Chausse, Frédéric; Aufrère, Romuald; Chapuis, Roland

doi:10.1109/iros.2006.282028

Cited by 69 publications

(54 citation statements)

References 12 publications

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“…While recent research efforts have primarily focused on reducing the computational complexity of EKF-based CL [10][11][12][13], the fundamental issue of consistency has received little attention. As defined in [14], a state estimator is consistent if the estimation errors are zero-mean, and have covariance smaller than or equal to the one calculated by the filter.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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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 observabilityconstrained (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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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“…Robot i can ensure this by communicating this information to all robots personally, or by communicating with another robot in the team that has satisfied conditions (i) and (ii) above, whichever happens earlier 7 .…”

Section: ) Communication Graph Connectivity Analysismentioning

confidence: 99%

“…From the union of (E1) and (E2) we have: 7 In the scheme proposed in [11], robot i discards the raw measurements at time-step k, as soon as it has calculated the centralized estimate for this time step and then communicates this centralized estimate to other robots. Note that irrespective of whether the raw measurements themselves or the centralized estimate computed from these measurements are communicated, the information content remains the same.…”

Section: ) Expected Time Delay Analysismentioning

confidence: 99%

“…Sub-optimal EKF-based algorithms, that do not require uninterrupted inter-robot communication, are presented in [6], using the Interlaced Kalman filter, and in [7], using stateestimates exchange. An approach based on a hierarchy of EKFs is proposed in [8] where the robot group is divided into sub-teams with leaders estimating the state of their sub-team using an EKF.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Asynchronous Multi-Centralized Cooperative Localization

Nerurkar

Roumeliotis

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a generalized framework for inter-robot information-transfer schemes in Multi-Centralized Cooperative Localization (MC-CL) under asynchronous communication, i.e., when the communication graph associated with the mobile robot network is time-varying and intermittently disconnected. Specifically, two information-transfer schemes, which differ based on their communication bandwidth requirements per link, are discussed. Even under asynchronous communication constraints, these schemes enable robots to compute pose estimates identical to those generated using the centralized CL framework, albeit delayed. For each of these schemes, necessary and sufficient conditions for the communicationgraph connectivity, that enable each robot to generate the centralized estimates, are developed. Moreover, detailed description of these schemes, along with their communication-complexity analysis and analytical results for the expected time delay in obtaining these estimates, are presented. Lastly, simulation results are used to validate the performance (the trade-off between communication link bandwidth and accuracy/delay) of these information-transfer schemes.

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“…While recent research efforts have focused on reducing the computational complexity of EKF-CL [7], [8], [9], the fundamental issue of consistency has received little attention. As defined in [10], a state estimator is consistent if the estimation errors are zero-mean and have covariance smaller than or equal to the one calculated by the filter.…”

Section: Introductionmentioning

confidence: 99%

On the consistency of multi-robot cooperative localization

Huang

Trawny

Mourikis

et al. 2009

Robotics: Science and Systems V

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Abstract-In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. To the best of our knowledge, this is the first work that analytically shows 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 observabilitybased methodology for designing consistent estimators and propose a novel Observability-Constrained (OC)-EKF. In contrast to the standard EKF-CL, the linearization points of the OC-EKF are selected so as to ensure that the dimension of the observable subspace remains the same as that of the original (nonlinear) system. The proposed OC-EKF has been tested in simulation and experimentally, and has been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.

show abstract

Localization of a Group of Communicating Vehicles by State Exchange

Cited by 69 publications

References 12 publications

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

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

Asynchronous Multi-Centralized Cooperative Localization

On the consistency of multi-robot cooperative localization

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