A nonparametric belief solution to the Bayes tree
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 co...
| Main Authors: | , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: | http://hdl.handle.net/1721.1/120482 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1811068808061779968 |
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| author | Fourie, Dehann Kaess, Michael Leonard, John J |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Fourie, Dehann Kaess, Michael Leonard, John J |
| author_sort | Fourie, Dehann |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:01:30Z |
| format | Article |
| id | mit-1721.1/120482 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:01:30Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1204822022-09-30T01:47:42Z A nonparametric belief solution to the Bayes tree Fourie, Dehann Kaess, Michael Leonard, John J Massachusetts Institute of Technology. Department of Mechanical Engineering Leonard, John J 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. 2019-02-19T18:06:15Z 2019-02-19T18:06:15Z 2016-12 2016-10 2018-12-12T14:35:16Z Article http://purl.org/eprint/type/ConferencePaper 978-1-5090-3762-9 http://hdl.handle.net/1721.1/120482 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, https://orcid.org/0000-0002-8863-6550 http://dx.doi.org/10.1109/IROS.2016.7759343 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other univ. web domain |
| spellingShingle | Fourie, Dehann Kaess, Michael Leonard, John J A nonparametric belief solution to the Bayes tree |
| title | A nonparametric belief solution to the Bayes tree |
| title_full | A nonparametric belief solution to the Bayes tree |
| title_fullStr | A nonparametric belief solution to the Bayes tree |
| title_full_unstemmed | A nonparametric belief solution to the Bayes tree |
| title_short | A nonparametric belief solution to the Bayes tree |
| title_sort | nonparametric belief solution to the bayes tree |
| url | http://hdl.handle.net/1721.1/120482 https://orcid.org/0000-0002-8863-6550 |
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