Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Localize Through Compressed Binary Maps   One of the main difficulties of scaling current localization systems to large
environments is the on-board storage required for the maps. In this paper we
propose to learn to compress the map representation such that it is optimal for
the localization task. As a consequence, higher compression rates can be
achieved without loss of localization accuracy when compared to standard coding
schemes that optimize for reconstruction, thus ignoring the end task. Our
experiments show that it is possible to learn a task-specific compression which
reduces storage requirements by two orders of magnitude over general-purpose
codecs such as WebP without sacrificing performance.

2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci