A multifrontal QR factorization approach to distributed inference applied to multi-robot localization and mapping (2005)
| Venue: | in Proceedings of the American Association for Artificial Intelligence |
| Citations: | 15 - 5 self |
BibTeX
@INPROCEEDINGS{Dellaert05amultifrontal,
author = {Frank Dellaert and Peter Krauthausen},
title = {A multifrontal QR factorization approach to distributed inference applied to multi-robot localization and mapping},
booktitle = {in Proceedings of the American Association for Artificial Intelligence},
year = {2005},
pages = {1261--1266}
}
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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.
