Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PROBE-GK: Predictive Robust Estimation using Generalized Kernels   Many algorithms in computer vision and robotics make strong assumptions about
uncertainty, and rely on the validity of these assumptions to produce accurate
and consistent state estimates. In practice, dynamic environments may degrade
sensor performance in predictable ways that cannot be captured with static
uncertainty parameters. In this paper, we employ fast nonparametric Bayesian
inference techniques to more accurately model sensor uncertainty. By setting a
prior on observation uncertainty, we derive a predictive robust estimator, and
show how our model can be learned from sample images, both with and without
knowledge of the motion used to generate the data. We validate our approach
through Monte Carlo simulations, and report significant improvements in
localization accuracy relative to a fixed noise model in several settings,
including on synthetic data, the KITTI dataset, and our own experimental
platform.

21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
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The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol