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- W199146695 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." @default.
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- W199146695 date "2005-07-09" @default.
- W199146695 modified "2023-09-25" @default.
- W199146695 title "A multifrontal QR factorization approach to distributed inference applied to multirobot localization and mapping" @default.
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