Variational Filtering with Copula Models for SLAM

@article{Martin2020VariationalFW,
  title={Variational Filtering with Copula Models for SLAM},
  author={John D. Martin and Kevin Anthony James Doherty and Caralyn Cyr and Brendan Englot and John J. Leonard},
  journal={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  pages={5066-5073}
}
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… 

Figures from this paper

Online Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

—This paper presents normalizing flows for incre- mental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement

NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows

This paper presents a novel non-Gaussian inference algorithm, Normalizing Flow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/or non-linear measurement models. NF-iSAM

References

SHOWING 1-10 OF 44 REFERENCES

FastSLAM: a factored solution to the simultaneous localization and mapping problem

This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map.

Simultaneous localization and mapping with multimodal probability distributions

A method to generate MoG constraints from a plane-based registration algorithm is introduced and used for 3D SLAM under ambiguities and is compared with traditional state-of-the-art optimization methods.

DP-SLAM 2.0

  • Austin I. EliazarRonald Parr
  • Computer Science
    IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
  • 2004
An improved map representation and laser penetration model, an improvement in the asymptotic efficiency of the algorithm, and empirical results of loop closing on a high resolution map of a very challenging domain are demonstrated.

Inference on networks of mixtures for robust robot mapping

This work proposes a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled, and shows that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases the “front-end” loop-validation systems can be unnecessary.

Multimodal Semantic SLAM with Probabilistic Data Association

This work proposes a solution that represents hypotheses as multiple modes of an equivalent non-Gaussian sensor model that solves the resulting non- Gaussian inference problem under ambiguous data associations using nonparametric belief propagation.

Exactly Sparse Delayed-State Filters

This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which

A Bayesian Algorithm for Simultaneous Localisation and Map Building

The Sum-of-Gaussian (SOG) method is used to approximate more general (arbitrary) probability distributions and permits the generalizations made possible by particle filter or Monte-Carlo methods, while inheriting the real-time computational advantages of the Kalman filter.

Estimating uncertain spatial relationships in robotics

A representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained, providing a general solution to the problem of estimating uncertain relative spatial relationships.

Incremental smoothing and mapping

iSAM provides an exact and efficient solution to the SLAM estimation problem while also addressing data association and presents an efficient algorithm to obtain the necessary estimation uncertainties in real-time based on the factored information matrix.

A UPF-UKF Framework For SLAM

  • Xiang WangHong Zhang
  • Computer Science
    Proceedings 2007 IEEE International Conference on Robotics and Automation
  • 2007
The proposed SLAM framework is more accurate than other popular SLAM frameworks while its efficiency is maintained and the algorithm avoids the calculation of the Jacobian for both motion model and the observation model, which could be extremely difficult for high order systems.