  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.
