Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Paper in proceeding, 2018
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applica-tions 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
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
Visual localization
camera pose estimation
long term localization
benchmark
Author
Torsten Sattler
Swiss Federal Institute of Technology in Zürich (ETH)
Will Maddern
University of Oxford
Carl Toft
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Akihiko Torii
Tokyo Institute of Technology
Lars Hammarstrand
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Erik Stenborg
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Daniel Safari
Tokyo Institute of Technology
Technical University of Denmark (DTU)
Masatoshi Okutomi
Tokyo Institute of Technology
Marc Pollefeys
Microsoft Corporation
Swiss Federal Institute of Technology in Zürich (ETH)
Josef Sivic
Czech Technical University in Prague
Institut National de Recherche en Informatique et en Automatique (INRIA)
Fredrik Kahl
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Lund University
Tomas Pajdla
Czech Technical University in Prague
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
10636919 (ISSN)
8601-8610 8578995978-153866420-9 (ISBN)
Conference on Computer Vision and Pattern Recognition 2018
Salt Lake City, USA,
Salt Lake City, USA,
COPPLAR CampusShuttle cooperative perception & planning platform
VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.
Areas of Advance
Information and Communication Technology
Subject Categories
Robotics
Probability Theory and Statistics
Computer Vision and Robotics (Autonomous Systems)
DOI
10.1109/CVPR.2018.00897