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Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
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DOI：10.1109/TRO.2022.3216498.png)
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Abstract
En 中文
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).
Keywords:
Simultaneous localization and mapping
Inference algorithms
Approximation algorithms
Task analysis
Particle separators
Belief propagation
Random variables
Bayes tree
distribution estimation
non-Gaussian
normalizing flows
SLAM
Journal
IF:
10.5
Papers:
3.3K
Citations:
2.8W