GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM
nferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximation...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024
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| Online Access: | https://hdl.handle.net/1721.1/153647 |
| _version_ | 1826196477688414208 |
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| author | Huang, Qiangqiang Leonard, John J. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Huang, Qiangqiang Leonard, John J. |
| author_sort | Huang, Qiangqiang |
| collection | MIT |
| description | nferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximation and particle filters, either lack expressiveness for representing non-Gaussian posteriors or suffer from performance degeneracy when estimating high-dimensional posteriors. Inspired by the complementary strengths of Gaussian approximation and particle filters–scalability and non-Gaussian estimation, respectively–we blend these two approaches to infer marginal posteriors in SLAM. Specifically, Gaussian approximation provides robot pose distributions on which particle filters are conditioned to sample landmark marginals. In return, the maximum a posteriori point among these samples can be used to reset linearization points in the nonlinear optimization solver of the Gaussian approximation, facilitating the pursuit of global optima. We demonstrate the scalability, generalizability, and accuracy of our algorithm for real-time full posterior inference on realworld range-only SLAM and object-based bearing-only SLAM datasets. |
| first_indexed | 2024-09-23T10:27:34Z |
| format | Article |
| id | mit-1721.1/153647 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:27:34Z |
| publishDate | 2024 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1536472024-09-20T19:15:39Z GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM Huang, Qiangqiang Leonard, John J. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory nferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximation and particle filters, either lack expressiveness for representing non-Gaussian posteriors or suffer from performance degeneracy when estimating high-dimensional posteriors. Inspired by the complementary strengths of Gaussian approximation and particle filters–scalability and non-Gaussian estimation, respectively–we blend these two approaches to infer marginal posteriors in SLAM. Specifically, Gaussian approximation provides robot pose distributions on which particle filters are conditioned to sample landmark marginals. In return, the maximum a posteriori point among these samples can be used to reset linearization points in the nonlinear optimization solver of the Gaussian approximation, facilitating the pursuit of global optima. We demonstrate the scalability, generalizability, and accuracy of our algorithm for real-time full posterior inference on realworld range-only SLAM and object-based bearing-only SLAM datasets. 2024-03-08T20:46:44Z 2024-03-08T20:46:44Z 2023-10-01 2024-03-08T20:23:03Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/153647 Huang, Qiangqiang and Leonard, John J. 2023. "GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM." 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). en 10.1109/iros55552.2023.10341889 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Creative Commons Attribution-Noncommercial-ShareAlike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE arxiv |
| spellingShingle | Huang, Qiangqiang Leonard, John J. GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM |
| title | GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM |
| title_full | GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM |
| title_fullStr | GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM |
| title_full_unstemmed | GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM |
| title_short | GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM |
| title_sort | gapslam blending gaussian approximation and particle filters for real time non gaussian slam |
| url | https://hdl.handle.net/1721.1/153647 |
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