Learning to Localize Through Compressed Binary Maps

Xinkai Wei, Ioan Andrei Barsan, Shenlong Wang, Julieta Martinez, Raquel Urtasun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 10316-10324

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.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Wei_2019_CVPR,
author = {Wei, Xinkai and Barsan, Ioan Andrei and Wang, Shenlong and Martinez, Julieta and Urtasun, Raquel},
title = {Learning to Localize Through Compressed Binary Maps},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}