A multifrontal QR factorization approach to distributed inference applied to multirobot localization and mapping

  • Authors:

  • Frank Dellaert;Alexander Kipp;Peter Krauthausen

  • Affiliations:

  • College of Computing, Georgia Institute of Technology, Atlanta, Georgia;College of Computing, Georgia Institute of Technology, Atlanta, Georgia;College of Computing, Georgia Institute of Technology, Atlanta, Georgia

  • Venue:
  • AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
  • Year:
  • 2005

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Abstract

QR factorization is most often used as a "black box" algorithm, but is in fact an elegant computation on a factor graph. By computing a rooted clique tree on this graph, the computation can be parallelized across subtrees, which forms the basis of so-called multifrontal QR methods. By judiciously choosing the order in which variables are eliminated in the clique tree computation, we show that one straightforwardly obtains a method for performing inference in distributed sensor networks. One obvious application is distributed localization and mapping with a team of robots. We phrase the problem as inference on a large-scale Gaussian Markov Random Field induced by the measurement factor graph, and show how multifrontal QR on this graph solves for the global map and all the robot poses in a distributed fashion. The method is illustrated using both small and large-scale simulations, and validated in practice through actual robot experiments.