Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition

Frederik Rahbæk Warburg*, Søren Hauberg, Manuel Lopez-Antequera, Pau Gargallo, Yubin Kuang, Javier Civera

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

20 Downloads (Pure)

Abstract

Lifelong place recognition is an essential and challenging task in computer vision, with vast applications in robust localization and efficient large-scale 3D reconstruction. Progress is currently hindered by a lack of large, diverse, publicly available datasets. We contribute with Mapillary Street-Level Sequences (MSLS), a large dataset for urban and suburban place recognition from image sequences. It contains more than 1.6 million images curated from the Mapillary collaborative mapping platform. The dataset is orders of magnitude larger than current data sources, and is designed to reflect the diversities of true lifelong learning. It features images from 30 major cities across six continents, hundreds of distinct cameras, and substantially different viewpoints and capture times, spanning all seasons over a nine-year period. All images are geo-located with GPS and compass, and feature high-level attributes such as road type. We propose a set of benchmark tasks designed to push state-of-the-art performance and provide baseline studies. We show that current state-of-the-art methods still have a long way to go, and that the lack of diversity in existing datasets has prevented generalization to new environments. The dataset and benchmarks are available for academic research.

Original languageEnglish
Title of host publicationProceedings of IEEE Conference on Computer Vision and Pattern Recognition 2020
Number of pages10
PublisherIEEE
Publication date2020
Publication statusPublished - 2020
EventIEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual
Duration: 14 Jun 202019 Jun 2020
http://cvpr2020.thecvf.com/

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2020
LocationVirtual
Period14/06/202019/06/2020
Internet address

Cite this

Warburg, F. R., Hauberg, S., Lopez-Antequera, M., Gargallo, P., Kuang, Y., & Civera, J. (2020). Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2020 IEEE.