Using Image Sequences for Long-Term Visual Localization
Paper i proceeding, 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


Erik Stenborg

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Torsten Sattler

Ceske Vysoke Uceni Technicke v Praze

Lars Hammarstrand

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

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

938-948 9320360

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


Robotteknik och automation

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling



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