@inproceedings{lianos2018vso,
author = {Lianos, Konstantinos-Nektarios and Schönberger, Johannes L. and Pollefeys, Marc and Sattler, Torsten},
title = {VSO: Visual Semantic Odometry},
booktitle = {2018 European Conference on Computer Vision},
year = {2018},
month = {September},
abstract = {Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation. Current state-of-the-art direct and indirect methods use short-term tracking to obtain continuous frame-to-frame constraints, while long-term constraints are established using loop closures. In this paper, we propose a novel visual semantic odometry (VSO) framework to enable medium-term continuous tracking of points using semantics. Our proposed framework can be easily integrated into existing direct and indirect visual odometry pipelines. Experiments on challenging real-world datasets demonstrate a significant improvement over state-of-the-art baselines in the context of autonomous driving simply by integrating our semantic constraints.},
url = {http://approjects.co.za/?big=en-us/research/publication/vso-visual-semantic-odometry/},
pages = {234-250},
}