Semantic Match Consistency for Long-Term Visual Localization
Paper in proceeding, 2018
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.
Visual localization
Outlier rejection
Camera pose estimation
Semantic segmentation
Self-driving cars
Author
Carl Toft
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Erik Stenborg
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Lars Hammarstrand
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Lucas Brynte
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Marc Pollefeys
Swiss Federal Institute of Technology in Zürich (ETH)
Microsoft
Torsten Sattler
Swiss Federal Institute of Technology in Zürich (ETH)
Fredrik Kahl
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
03029743 (ISSN) 16113349 (eISSN)
Vol. 11206 LNCS 391-408
15th European Conference on Computer Vision, ECCV 2018
Munich, Germany,
Munich, Germany,
COPPLAR CampusShuttle cooperative perception & planning platform
VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.
Subject Categories
Robotics
Computer Science
Computer Vision and Robotics (Autonomous Systems)
DOI
10.1007/978-3-030-01216-8_24