Using Image Sequences for Long-Term Visual Localization
Paper in proceedings, 2020

Estimating the pose of a camera in a known scene, i.e., visual localization, is a core task for applications such as self-driving cars. In many scenarios, image sequences are available and existing work on combining single-image localization with odometry offers to unlock their potential for improving localization performance. Still, the largest part of the literature focuses on single-image localization and ignores the availability of sequence data. The goal of this paper is to demonstrate the potential of image sequences in challenging scenarios, e.g., under day-night or seasonal changes. Combining ideas from the literature, we describe a sequence-based localization pipeline that combines odometry with both a coarse and a fine localization module. Experiments on long-term localization datasets show that combining single-image global localization against a prebuilt map with a visual odometry / SLAM pipeline improves performance to a level where the extended CMU Seasons dataset can be considered solved. We show that SIFT features can perform on par with modern state-of-the-art features in our framework, despite being much weaker and a magnitude faster to compute. Our code is publicly available at github.com/rulllars.

Author

Erik Stenborg

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Torsten Sattler

Czech Technical University in Prague

Lars Hammarstrand

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Proceedings - 2020 International Conference on 3D Vision, 3DV 2020

938-948 9320360

8th International Conference on 3D Vision, 3DV 2020
Virtual, Fukuoka, Japan,

Subject Categories

Robotics

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1109/3DV50981.2020.00104

More information

Latest update

3/15/2021