Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
(2018) 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 p.8601-8610- Abstract
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through... (More)
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.
(Less)
- author
- organization
- publishing date
- 2018-12-17
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
- article number
- 8578995
- pages
- 10 pages
- publisher
- IEEE Computer Society
- conference name
- 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
- conference location
- Salt Lake City, United States
- conference dates
- 2018-06-18 - 2018-06-22
- external identifiers
-
- scopus:85062877660
- ISBN
- 9781538664209
- DOI
- 10.1109/CVPR.2018.00897
- language
- English
- LU publication?
- yes
- id
- c8463e3b-b90c-47d4-900b-9a00a3abec36
- date added to LUP
- 2019-04-01 10:00:02
- date last changed
- 2022-04-25 22:03:38
@inproceedings{c8463e3b-b90c-47d4-900b-9a00a3abec36, abstract = {{<p>Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.</p>}}, author = {{Sattler, Torsten and Maddern, Will and Toft, Carl and Torii, Akihiko and Hammarstrand, Lars and Stenborg, Erik and Safari, Daniel and Okutomi, Masatoshi and Pollefeys, Marc and Sivic, Josef and Kahl, Fredrik and Pajdla, Tomas}}, booktitle = {{Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018}}, isbn = {{9781538664209}}, language = {{eng}}, month = {{12}}, pages = {{8601--8610}}, publisher = {{IEEE Computer Society}}, title = {{Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions}}, url = {{http://dx.doi.org/10.1109/CVPR.2018.00897}}, doi = {{10.1109/CVPR.2018.00897}}, year = {{2018}}, }