Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep-Geometric 6 DoF Localization from a Single Image in Topo-metric
  Maps   We describe a Deep-Geometric Localizer that is able to estimate the full 6
Degree of Freedom (DoF) global pose of the camera from a single image in a
previously mapped environment. Our map is a topo-metric one, with discrete
topological nodes whose 6 DoF poses are known. Each topo-node in our map also
comprises of a set of points, whose 2D features and 3D locations are stored as
part of the mapping process. For the mapping phase, we utilise a stereo camera
and a regular stereo visual SLAM pipeline. During the localization phase, we
take a single camera image, localize it to a topological node using Deep
Learning, and use a geometric algorithm (PnP) on the matched 2D features (and
their 3D positions in the topo map) to determine the full 6 DoF globally
consistent pose of the camera. Our method divorces the mapping and the
localization algorithms and sensors (stereo and mono), and allows accurate 6
DoF pose estimation in a previously mapped environment using a single camera.
With potential VR/AR and localization applications in single camera devices
such as mobile phones and drones, our hybrid algorithm compares favourably with
the fully Deep-Learning based Pose-Net that regresses pose from a single image
in simulated as well as real environments.

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The categories of this paper are: math
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: bayes-an