A nonparametric belief solution to the Bayes tree
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Fourie16iros.pdf
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Author(s)
Fourie, Dehann
Kaess, Michael
Leonard, John J
Date Issued
December 2016
Journal
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Fourie, Dehann, John Leonard, and Michael Kaess. “A Nonparametric Belief Solution to the Bayes Tree.” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9-14 October, 2016, Daejeon, South Korea, IEEE, 2016,
Version
Author's final manuscript
Abstract
We relax parametric inference to a nonparametric representation towards more general solutions on factor graphs. We use the Bayes tree factorization to maximally exploit structure in the joint posterior thereby minimizing computation. We use kernel density estimation to represent a wider class of constraint beliefs, which naturally encapsulates multi-hypothesis and non-Gaussian inference. A variety of new uncertainty models can now be directly applied in the factor graph, and have the solver recover a potentially multimodal posterior. For example, data association for loop closure proposals can be incorporated at inference time without further modifications to the factor graph. Our implementation of the presented algorithm is written entirely in the Julia language, exploiting high performance parallel computing. We show a larger scale use case with the well known Victoria park mapping and localization data set inferring over uncertain loop closures.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/IROS.2016.7759343
