Variational Filtering with Copula Models for SLAM
Name
2008.00504.pdf
Description
Submitted version
Size
1.08 MB
Format
Unknown
Checksum (MD5)
85162a1b4997005ae2a21c815f27e512
Author(s)
Martin, John D.
Doherty, Kevin
Cyr, Caralyn
Englot, Brendan
Leonard, John
Date Issued
February 2021
Journal
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Martin, John D, Doherty, Kevin, Cyr, Caralyn, Englot, Brendan and Leonard, John. 2020. "Variational Filtering with Copula Models for SLAM." IEEE International Conference on Intelligent Robots and Systems.
Version
Original manuscript
Abstract
© 2020 IEEE. The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros45743.2020.9341404
