Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation
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iros2020_fourie.pdf
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Accepted version
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3.04 MB
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Author(s)
Fourie, Dehann
Rypkema, Nicholas R
Teixeira, Pedro Vaz
Claassens, Sam
Fischell, Erin
Leonard, John
Date Issued
2020
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Fourie, Dehann, Rypkema, Nicholas R, Teixeira, Pedro Vaz, Claassens, Sam, Fischell, Erin et al. 2020. "Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation." IEEE International Conference on Intelligent Robots and Systems.
Version
Author's final manuscript
Abstract
© 2020 IEEE. This paper presents a method for processing sparse, non-Gaussian multimodal data in a simultaneous localization and mapping (SLAM) framework using factor graphs. Our approach demonstrates the feasibility of using a sum-product inference strategy to recover functional belief marginals from highly non-Gaussian situations, relaxing the prolific unimodal Gaussian assumption. The method is more focused than conventional multi-hypothesis approaches, but still captures dominant modes via multi-modality. The proposed algorithm exists in a trade space that spans the anticipated uncertainty of measurement data, task-specific performance, sensor quality, and computational cost. This work leverages several major algorithm design constructs, including clique recycling, to put an upper bound on the allowable computational expense - a major challenge in non-parametric methods. To better demonstrate robustness, experimental results show the feasibility of the method on at least two of four major sources of non-Gaussian behavior: i) the first introduces a canonical range-only problem which is always underdetermined although composed exclusively from Gaussian measurements; ii) a real-world AUV dataset, demonstrating how ambiguous acoustic correlator measurements are directly incorporated into a non-Gaussian SLAM solution, while using dead reckon tethering to overcome short term computational requirements.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Woods Hole Oceanographic Institution
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1109/IROS45743.2020.9341490
