Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Estimation of Dense Optical-Flow for Robust Visual
  Navigation   This paper presents a novel dense optical-flow algorithm to solve the
monocular simultaneous localization and mapping (SLAM) problem for ground or
aerial robots. Dense optical flow can effectively provide the ego-motion of the
vehicle while enabling collision avoidance with the potential obstacles.
Existing work has not fully utilized the uncertainty of the optical flow -- at
most an isotropic Gaussian density model. We estimate the full uncertainty of
the optical flow and propose a new eight-point algorithm based on the
statistical Mahalanobis distance. Combined with the pose-graph optimization,
the proposed method demonstrates enhanced robustness and accuracy for the
public autonomous car dataset (KITTI) and aerial monocular dataset.

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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph