Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
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2110.00876.pdf
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Author(s)
Huang, Qiangqiang
Pu, Can
Khosoussi, Kasra
Rosen, David M.
Fourie, Dehann
How, Jonathan P.
Leonard, John J.
Date Issued
April 2023
Journal
IEEE Transactions on Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Huang, Qiangqiang, Pu, Can, Khosoussi, Kasra, Rosen, David M., Fourie, Dehann et al. 2023. "Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows." IEEE Transactions on Robotics, 39 (2).
Version
Author's final manuscript
Abstract
This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association).
Subjects
Electrical and Electronic Engineering
Computer Science Applications
Control and Systems Engineering
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1109/tro.2022.3216498
