GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM
Name
2303.14283v2.pdf
Size
5.29 MB
Format
Adobe PDF
Checksum (MD5)
a8c408262cc41c1f94c0f737ab984bb0
Author(s)
Huang, Qiangqiang
Leonard, John J.
Date Issued
October 1, 2023
Journal
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Citation
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).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros55552.2023.10341889
