Efficient Localisation Using Images and OpenStreetMaps
Mengjie Zhou, Xieyuanli Chen, Noe Samano, Cyrill Stachniss, Andrew Calway
Abstract
The ability to localise is key for robot navigation. We describe an efficient method for vision-based localisation, which combines sequential Monte Carlo tracking with matching ground-level images to 2-D cartographic maps such as OpenStreetMaps. The matching is based on a learned embedded space representation linking images and map tiles, encoding the common semantic information present in both and providing potential for invariance to changing conditions. Moreover, the compactness of 2-D maps supports scalability. This contrasts with the majority of previous approaches based on matching with single-shot geo-referenced images or 3-D reconstructions. We present experiments using the StreetLearn and Oxford RobotCar datasets and demonstrate that the method is highly effective, giving high accuracy and fast convergence.
BibTeX
@inproceedings{iros2021_efficientlocalis,
title = {Efficient Localisation Using Images and OpenStreetMaps},
author = {Mengjie Zhou and Xieyuanli Chen and Noe Samano and Cyrill Stachniss and Andrew Calway},
booktitle = {IROS 2021},
year = {2021}
}