Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization   Visual Odometry (VO) accumulates a positional drift in long-term robot
navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in
various aspects, VO still suffers from moving obstacles, discontinuous
observation of features, and poor textures or visual information. While recent
approaches estimate a 6DoF pose either directly from (a series of) images or by
merging depth maps with optical flow (OF), research that combines absolute pose
regression with OF is limited. We propose ViPR, a novel modular architecture
for long-term 6DoF VO that leverages temporal information and synergies between
absolute pose estimates (from PoseNet-like modules) and relative pose estimates
(from FlowNet-based modules) by combining both through recurrent layers.
Experiments on known datasets and on our own Industry dataset show that our
modular design outperforms state of the art in long-term navigation tasks.

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The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math