An Experimental Study of Robust Distributed Multi-robot Data Association from Arbitrary Poses

  • Erik Nelson
  • Vadim Indelman
  • Nathan Michael
  • Frank Dellaert
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 109)

Abstract

In this work, we experimentally investigate the problem of computing the relative transformation between multiple vehicles from corresponding inter-robot observations during autonomous operation in a common unknown environment. Building on our prior work, we consider an EM-based methodology which evaluates sensory observations gathered over vehicle trajectories to establish robust relative pose transformations between robots. We focus on experimentally evaluating the performance of the approach as well as its computational complexity and shared data requirements using multiple autonomous vehicles (aerial robots). We describe an observation subsampling technique which utilizes laser scan autocovariance to reduce the total number of observations shared between robots. Employing this technique reduces run time of the algorithm significantly, while only slightly diminishing the accuracies of computed inter-robot transformations. Finally, we provide discussion on data transfer and the feasibility of implementing the approach on a mesh network.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Erik Nelson
    • 1
  • Vadim Indelman
    • 2
  • Nathan Michael
    • 1
  • Frank Dellaert
    • 2
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.Georgia Institute of TechnologyAtlantaUSA

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