Interrobot Transformations in 3-D

Trawny, Nikolas; Zhou, Xun; Zhou, Ke; Roumeliotis, Stergios I.

doi:10.1109/tro.2010.2042539

Cited by 58 publications

(45 citation statements)

References 40 publications

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“…To date, theoretical studies on the properties of CL have focused on issues such as initialization [16][17][18][19], system observability [20,16,18], accuracy bounds [21,22], and the complexity of deterministic (static) robot network localization [23]. However, to the best of our knowledge, prior to [15], no work has analytically examined the consistency of CL.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

et al. 2010

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“…Different estimation strategies have been proposed for this problem, such as the use of hierarchically distributed Extended Kalman filters (Martinelli, 2007) or closed-form solvers in conjunction with (nonlinear) iterative least squares, both in the 2D (Zhou and Roumeliotis, 2008) and the 3D case (Trawny et al, 2010). An interesting study on the role of the communication in a decentralized localization algorithm has been proposed by Leung et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Mutual localization in multi-robot systems using anonymous relative measurements

Franchi

Oriolo

Stegagno

2013

The International Journal of Robotics Research

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Abstract-We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e., measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.

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“…Alternatively, each agent can locally combine its observations and build an estimate of the relative full-position of its neighbors using e.g., the approach described in [8,9] for 3D scenarios. When full-position measurements are available, the localization problem becomes linear and can thus be solved by using linear optimization methods [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Distributed centroid estimation from noisy relative measurements

Aragüés

Carlone

Sagüés

et al. 2012

Systems & Control Letters

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We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisy relative measurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents positions are estimated with respect to the anchor.We show that our centroid-based algorithm converges to the optimal solution, and that such a centroid-based representation produces results that are more accurate than anchor-based ones, irrespective of the selected anchor.

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Interrobot Transformations in 3-D

Cited by 58 publications

References 40 publications

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

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

Mutual localization in multi-robot systems using anonymous relative measurements

Distributed centroid estimation from noisy relative measurements

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