Semantic Match Consistency for Long-Term Visual Localization
Paper in proceedings, 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.

Self-driving cars

Outlier rejection

Semantic segmentation

Camera pose estimation

Visual localization

Author

Carl Toft

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Erik Stenborg

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Lars Hammarstrand

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Lucas Brynte

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

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, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 11206 LNCS 391-408

15th European Conference on Computer Vision, ECCV 2018
Munich, Germany,

Subject Categories

Robotics

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/978-3-030-01216-8_24

More information

Latest update

12/28/2018