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. I.

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

Abstract—In recent years underwater survey and surveillancemissionswithmore thanasingleAutonomousUnderwater Vehicle (AUV) have become more common thanks to more reliableandcheaper platforms, as wellas theadditionof remote command and control communications using, for example, the WHOI acoustic modem. However cooperative navigation of AUVs has thus far been limited to a single AUV supported by a dedicated surface vehicle with access to GPS. In this paper a scalable and modular framework is presented in which any number of vehicles can broadcast, forward and acknowledge range, dead-reckoning, feature and GPS measurements so that the full fleet of AUVs can navigate and cooperate in a consistent and accurate manner. The approach is independent of the resultantapplication—suchasrecursive stateestimationorfull pose optimization. Trade-offs between the number of vehicles, the condition of the communication channel and rate at which updates are available are also discussed. Finally performance is illustrated in a realistic experiment. I.

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 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.

This thesis would not have been possible without the support of a number of people. First of all, my thanks go to my adviser, Professor Stergios Roumeliotis, for his constant encouragement and guidance, for the long hours of passing along his knowledge and experience, for pushing me beyond my own limitations, and for his seemingly endless supply of interesting research problems. I am also thankful for the time and invaluable advice from