Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Filtering with Copula Models for SLAM   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.

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The categories of this paper are: math-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat