Learning to Localize Through Compressed Binary Maps

by   Xinkai Wei, et al.

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.



page 11

page 12

page 13

page 14

page 15

page 16

page 17

page 18


Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

In this paper we propose a novel semantic localization algorithm that ex...

Image compression optimized for 3D reconstruction by utilizing deep neural networks

Computer vision tasks are often expected to be executed on compressed im...

Vignette: Perceptual Compression for Video Storage and Processing Systems

Compressed videos constitute 70 rates far outpace compute and storage im...

Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images

Storage systems often rely on multiple copies of the same compressed dat...

GIRAF: General purpose In-storage Resistive Associative Framework

GIRAF is an in-storage architecture and algorithm framework based on Res...

End-to-end Learning of Compressible Features

Pre-trained convolutional neural networks (CNNs) are powerful off-the-sh...

DZip: improved general-purpose lossless compression based on novel neural network modeling

We consider lossless compression based on statistical data modeling foll...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.