Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Satellite Image-based Localization via Learned Embeddings   We propose a vision-based method that localizes a ground vehicle using
publicly available satellite imagery as the only prior knowledge of the
environment. Our approach takes as input a sequence of ground-level images
acquired by the vehicle as it navigates, and outputs an estimate of the
vehicle's pose relative to a georeferenced satellite image. We overcome the
significant viewpoint and appearance variations between the images through a
neural multi-view model that learns location-discriminative embeddings in which
ground-level images are matched with their corresponding satellite view of the
scene. We use this learned function as an observation model in a filtering
framework to maintain a distribution over the vehicle's pose. We evaluate our
method on different benchmark datasets and demonstrate its ability localize
ground-level images in environments novel relative to training, despite the
challenges of significant viewpoint and appearance variations.

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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an