Using Image Sequences for Long-Term Visual Localization
| Autor: | Lars Hammarstrand, Erik Stenborg, Torsten Sattler |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Scale-invariant feature transform 02 engineering and technology Simultaneous localization and mapping Pipeline (software) Term (time) Visualization 020901 industrial engineering & automation Odometry 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Visual odometry business Pose |
| Zdroj: | 3DV |
| DOI: | 10.1109/3dv50981.2020.00104 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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