Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Place Recognition with Event-based Cameras and a Neural Implementation
  of SeqSLAM   Event-based cameras offer much potential to the fields of robotics and
computer vision, in part due to their large dynamic range and extremely high
"frame rates". These attributes make them, at least in theory, particularly
suitable for enabling tasks like navigation and mapping on high speed robotic
platforms under challenging lighting conditions, a task which has been
particularly challenging for traditional algorithms and camera sensors. Before
these tasks become feasible however, progress must be made towards adapting and
innovating current RGB-camera-based algorithms to work with event-based
cameras. In this paper we present ongoing research investigating two distinct
approaches to incorporating event-based cameras for robotic navigation: the
investigation of suitable place recognition / loop closure techniques, and the
development of efficient neural implementations of place recognition techniques
that enable the possibility of place recognition using event-based cameras at
very high frame rates using neuromorphic computing hardware.

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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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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: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: adap-org