Learning to Localize Through Compressed Binary Maps

TL;DR

This paper introduces a task-specific compression method for map representations in localization systems, significantly reducing storage needs by two orders of magnitude without compromising accuracy, thereby enabling scalable large-environment localization.

Contribution

It presents a novel learned compression approach tailored for localization maps, outperforming general codecs in efficiency while maintaining accuracy.

Findings

Achieves 100x reduction in map storage compared to WebP.

Maintains localization accuracy despite high compression rates.

Demonstrates effectiveness in large-scale environments.

Abstract

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.