Non-parametric Mixed-Manifold Products using Multiscale Kernel Densities
Name
fourie_iros19_manifolds.pdf
Description
Accepted version
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658.11 KB
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Unknown
Checksum (MD5)
5ccc73f7b116e00e7770c501f666ae0e
Author(s)
Fourie, Dehann
Teixeira, Pedro Vaz
Leonard, John J
Date Issued
November 2019
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
IEEE
Citation
Fourie, Dehann, Teixeira, Pedro Vaz and Leonard, John. 2019. "Non-parametric Mixed-Manifold Products using Multiscale Kernel Densities." IEEE International Conference on Intelligent Robots and Systems.
Version
Author's final manuscript
Abstract
© 2019 IEEE. We extend the core operation of non-parametric belief propagation (NBP), also known as multi-scale sequential Gibbs sampling, to approximate products of kernel density estimated beliefs that reside on some manifold. The original algorithm, though multidimensional, implicitly assumes the beliefs to reside on the Euclidean mathbb{R}{d} space only. The proposed extension generalizes to any mixture of Riemannian manifolds, provided the primary operations - addition and subtraction - are defined. Our motivation is primarily focused on state-estimation using non-Gaussian factor graphs for multimodal simultaneous localization and mapping in robotics. The paper presents the method as well as simulation and experimental results for validation. Our implementation is publicly available and allows for expansion with user-defined manifold mixtures.
MIT Department
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/iros40897.2019.8968209
