Learning to Localize Through Compressed Binary Maps
Xinkai Wei, Ioan Andrei Barsan, Shenlong Wang, Julieta Martinez, Raquel Urtasun
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.
BibTeX
@inproceedings{cvpr2019_learningtolocali,
title = {Learning to Localize Through Compressed Binary Maps},
author = {Xinkai Wei and Ioan Andrei Barsan and Shenlong Wang and Julieta Martinez and Raquel Urtasun},
booktitle = {CVPR 2019},
year = {2019}
}