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Fault-Tolerant Covariance Intersection for Localizing Robot Swarms

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Distributed Autonomous Robotic Systems

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 9))

Abstract

This paper examines the important problem of cooperative localization in robot swarms, in the presence of unmodeled errors experienced by real sensors in hardware platforms. Many existing methods for cooperative swarm localization rely on approximate distance metric heuristics based on properties of the communication graph. We present a new cooperative localization method that is based on a rigorous and scalable treatment of estimation errors generated by peer-to-peer sharing of relative robot pose information. Our approach blends Covariance Intersection and Covariance Union techniques from distributed sensor fusion theory in a novel way, in order to maintain statistical estimation consistency for cooperative localization errors. Experimental validation results show that this approach provides both reliable and accurate state estimation results for Droplet swarms in scenarios where other existing swarm localization methods cannot.

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Notes

  1. 1.

    The squared Mahalanobis distance between an observation, \(\mathbf {x}\) and a multinormal distribution with mean \(\mathbf {\mu }\) and covariance \(\mathbf {\Sigma }^{}_{}\) is defined as  [8].

  2. 2.

    http://github.com/correlllab/cu-droplet

  3. 3.

    Updates with a Mahalanobis distance greater than 4 (\(d>d_{\mathrm {TOSS}}\)) were still ignored.

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Acknowledgements

This research has been supported by NSF grant #1150223.

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Authors and Affiliations

  1. Department of Computer Science, University of Colorado, Boulder, USA

    John Klingner, Nisar Ahmed & Nikolaus Correll

Authors
  1. John Klingner
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  2. Nisar Ahmed
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  3. Nikolaus Correll
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Corresponding author

Correspondence to John Klingner .

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Editors and Affiliations

  1. Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA

    Nikolaus Correll

  2. Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA

    Mac Schwager

  3. Department of Aerospace Engineering, University of Maryland, College Park, MD, USA

    Michael Otte

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Klingner, J., Ahmed, N., Correll, N. (2019). Fault-Tolerant Covariance Intersection for Localizing Robot Swarms. In: Correll, N., Schwager, M., Otte, M. (eds) Distributed Autonomous Robotic Systems. Springer Proceedings in Advanced Robotics, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-05816-6_34

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