Semantic Match Consistency for Long-Term Visual Localization

Carl Toft, Erik Stenborg, Lars Hammarstrand, Lucas Brynte, Marc Pollefeys, Torsten Sattler, Fredrik Kahl; The European Conference on Computer Vision (ECCV), 2018, pp. 383-399


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.

Related Material

author = {Toft, Carl and Stenborg, Erik and Hammarstrand, Lars and Brynte, Lucas and Pollefeys, Marc and Sattler, Torsten and Kahl, Fredrik},
title = {Semantic Match Consistency for Long-Term Visual Localization},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}