Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Any Way You Look At It: Semantic Crossview Localization and Mapping with
  LiDAR   Currently, GPS is by far the most popular global localization method.
However, it is not always reliable or accurate in all environments. SLAM
methods enable local state estimation but provide no means of registering the
local map to a global one, which can be important for inter-robot collaboration
or human interaction. In this work, we present a real-time method for utilizing
semantics to globally localize a robot using only egocentric 3D semantically
labelled LiDAR and IMU as well as top-down RGB images obtained from satellites
or aerial robots. Additionally, as it runs, our method builds a globally
registered, semantic map of the environment. We validate our method on KITTI as
well as our own challenging datasets, and show better than 10 meter accuracy, a
high degree of robustness, and the ability to estimate the scale of a top-down
map on the fly if it is initially unknown.

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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: eess
8
The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org