Semantic Match Consistency for Long-Term Visual Localization
Carl Toft, Erik Stenborg, Lars Hammarstrand, Lucas Brynte, Marc Pollefeys, Torsten Sattler, Fredrik Kahl
Abstract
Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots. Traditional feature-based methods often struggle in these conditions due to the significant number of erroneous matches between the image and the 3D model. In this paper, we present a method for scoring the individual correspondences by exploiting semantic information about the query image and the scene. In this way, erroneous correspondences tend to get a low semantic consistency score, whereas correct correspondences tend to get a high score. By incorporating this information in a standard localization pipeline, we show that the localization performance can be significantly improved compared to the state-of-the-art, as evaluated on two challenging long-term localization benchmarks.
BibTeX
@inproceedings{eccv2018_semanticmatchcon,
title = {Semantic Match Consistency for Long-Term Visual Localization},
author = {Carl Toft and Erik Stenborg and Lars Hammarstrand and Lucas Brynte and Marc Pollefeys and Torsten Sattler and Fredrik Kahl},
booktitle = {ECCV 2018},
year = {2018}
}