Learning to Localize through Compressed Binary Maps

Xinkai Wei, Ioan Andrei Barsan, Shenlong Wang, Julieta Martinez, Raquel Urtasun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PublisherIEEE Computer Society
Pages10308-10316
Number of pages9
ISBN (Electronic)9781728132938
DOIs
StatePublished - Jun 2019
Externally publishedYes
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2019-June
ISSN (Print)1063-6919

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Country/TerritoryUnited States
CityLong Beach
Period6/16/196/20/19

Keywords

  • Representation Learning
  • Robotics + Driving

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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